System and method for providing alerts optimized for a user

ABSTRACT

Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 15/684,361, filed Aug. 23, 2017, which is a divisional of U.S.application Ser. No. 15/582,467, filed Apr. 28, 2017, now U.S. Pat. No.10,052,073, which is a continuation of U.S. application Ser. No.15/582,057, filed Apr. 28, 2017, now U.S. Pat. No. 10,328,204, whichclaims the benefit of U.S. Provisional Application No. 62/330,729, filedMay 2, 2016. Each of the aforementioned applications is incorporated byreference herein in its entirety, and each is hereby expressly made apart of this specification.

FIELD

Alerts for users, particularly in the medical field where physiologicalparameters are being monitored, are provided.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high glucose, which may cause an array ofphysiological derangements (for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye) associated with the deteriorationof small blood vessels.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricks to obtain blood samples for measurement. Due to the lack ofcomfort and convenience associated with finger pricks, a person withdiabetes normally only measures his or her glucose levels two to fourtimes per day. Unfortunately, time intervals between measurements can bespread far enough apart that the person with diabetes finds out too lateof a hyperglycemic or hypoglycemic condition, sometimes incurringdangerous side effects. It is not only unlikely that a person withdiabetes will take a timely SMBG value, it is also likely that he or shewill not know if his or her blood glucose value is going up (higher) ordown (lower) based on conventional methods. Diabetics thus may beinhibited from making educated insulin therapy decisions.

Another device that some diabetics use to monitor their blood glucose isa continuous analyte sensor. A continuous analyte sensor typicallyincludes a sensor that is placed subcutaneously, transdermally (e.g.,transcutaneously), or intravascularly. The sensor measures theconcentration of a given analyte within the body, and generates a rawsignal that is transmitted to electronics associated with the sensor.The raw signal is converted into an output value that is displayed on adisplay. The output value that results from the conversion of the rawsignal is typically expressed in a form that provides the user withmeaningful clinical information, such as glucose expressed in mg/dL.

Where the analyte is glucose, and in the case of continuous glucosemonitors (CGM), some CGMs provide for the activation of various alertsor alarms when the user's glucose value enters dangerous or undesiredranges. For example, many CGM's provide alerts if a user's glucosevalues stray into a range of mild hypoglycemia or hyperglycemia, andalarms if the situation becomes more dire. In some cases, suchalerts/alarms use predictive algorithms to determine if the user isapproaching a dangerous state and thus if an alert or alarms should beactivated.

While useful, such alerts and alarms are not without problems. Forexample, users can quickly grow used to such alerts and alarms and beginto “tune them out” or otherwise ignore them. In some cases, users areunnecessarily re-alerted to a condition of which they are already aware.In many of these cases, “alert fatigue” can set in, causing the user toeither disregard the alert or turn the same off without adequateconsideration as to its cause or potential steps to address.

Other problems are now described. Examples will first be given in thecategory of “high” glucose alerts. In post meal time frames, users areoften annoyed when they receive high alerts after eating and subsequentto dosing for a meal. Such high alerts can even occasionally lead to“stacking” or dosing insulin when insulin is already “on board”. In suchcases, users are receiving alerts that they do not need to take actionfor, or which can cause users to take unnecessary action. In many cases,responses to such unnecessary post-meal alerts include that the userstarts ignoring alerts, sets higher alert thresholds (and thus preventsthe user from using a proper threshold as their target range boundary),or in some cases even turning off their high alerts. Such remedies cancause users to miss future unexpected high glucose levels.

Unnecessary re-alerts are another example of a high alert problem. Inthis case, users are annoyed when they receive multiple high alerts forthe same height glucose event. Such situations are often caused byglucose levels hovering above and below their high threshold. In somecases users can activate a “snooze” time, and such as some degree ofeffectiveness. However, as with post-meal alerts, users do not want tobe re-alerted for the same high event before their set snooze time.Remedies for these situations are similar to those above, including thatusers ignore alerts or turn off their high alert, again missing futureunexpected high glucose levels.

Another “high alert” problem includes missed boluses. For example, usersoften receive a high alert if they have forgotten to dose for a meal.The high alert reminds users to dose, but the same is typically too lateand does not prevent further rising. Remedies for missed boluses includethat users set lower high alert thresholds, or set a rise rate alert.However, such remedies may result in additional false alerts for theuser. In addition, the high alert and rise rate alert are sometimes noteffective or accurate enough to catch missed boluses.

Another “high alert” problem is that certain users, e.g., those with agoal of tighter glucose control, want to be alerted if they are closebut to below their high threshold for a long period of time. Such usersmay be using their high alert thresholds for their target zoneboundaries, and in such cases, users may be unaware of how to accuratelyset or change their high alert thresholds.

Other high alert problems include that users do not know how to react totheir initial alert settings. Still other high alert problems will alsobe understood.

Other problems exist in the use of “low alerts”. For example, alertfatigue as noted above can lead to mistrust in the system. For example,users may set a higher low alert threshold in order to give themselvesmore time to prevent severe hypoglycemic events. However, this may leadto more frequent alerts and consequent annoyance. For example, suchusers may receive many alerts of low blood glucose levels that do notlead to severe lows. While users desire more warnings for severe lows,frequent low alerts at a higher alert threshold cause mistrust in thesystem.

Relatedly, false alerts caused by faults such as compression may alsocause mistrust in the system. In response to alert fatigue, userssometimes set lower alert thresholds, but then they have consequentlyless time to prevent urgent lows. As another remedy, users may turnofflow alerts and use fall rate alerts or urgent low alerts instead. Forexample, a fall rate alert may be set at −2 or −3 mg/dL. As yet anotherremedy, users may turn off their low alerts and rely on urgent lowalerts instead. In many of these cases, user responses do not preventlow blood sugars.

Another “low alert” problem is similar to a high alert problem, andconstitutes the issue of unnecessary re-alerts. That is, users areannoyed when they receive multiple low alerts for the same low glucoseevent. In many cases, such unnecessary re-alerts are caused by theirglucose levels hovering just above or below their low threshold. Suchmay also be caused when users go above 55 but are still below their lowthreshold. In reaction to unnecessary re-alerts, users sometimes startignoring alerts, or may turn off their low alert, or may over treattheir condition, e.g., stacking carbs (which is often a particularproblem at night). But such remedies cause the user to miss futureunexpected low glucose levels.

Other low alert problems include that users may set their low alertthreshold as the bottom boundary for their target range. Other low alertproblems will also be understood.

Prior art in the field has dealt with certain alerting issues in thefollowing ways.

In one way, as disclosed in U.S. Patent Publication No. US-2015/0289821,filed 16 Mar. 2015 and entitled GLYCEMIC URGENCY ASSESSMENT AND ALERTSINTERFACE, an actionable alert is disclosed as being provided based on aglycemic urgency index, which is a value that is more representative ofa user's diabetic state than just a glucose value. Another publication,U.S. Patent Publication No. US-2014/0118138, granted as U.S. Pat. No.9,119,528 on 1 Sep. 2015, and entitled SYSTEMS AND METHODS FOR PROVIDINGSENSITIVE AND SPECIFIC ALARMS, discusses the occurrence of alarms thatmay be annoying to a user, but is directed to remedies such as waiting aparticular time period or using a time delay. In yet anotherapplication, U.S. Patent Publication No. US-2015/0119655, filed 28 Oct.2014 and entitled ADAPTIVE INTERFACE FOR CONTINUOUS MONITORING DEVICES,a user interface is adapted according to certain inputs, e.g., goals,population data, and the like. However, there is no disclosure ofadapting the alerts themselves. In yet a further application, U.S.Patent Publication No. US-2014/0012510, filed 13 Mar. 2013 and entitledSYSTEMS AND METHODS FOR LEVERAGING SMARTPHONE FEATURES IN CONTINUOUSGLUCOSE MONITORING, disclosures are provided such that, e.g., if theuser is in a meeting, an alert may be silenced. The reference discloseschanging the timing of an alert, but only as part of a global setting,and not on a real-time basis. In yet another application, USSN62/289,825, filed 1 Feb. 2016, and entitled SYSTEM AND METHOD FORDECISION SUPPORT USING LIFESTYLE FACTORS, feedback is provided to theuser for decision-support purposes, e.g., informing the user ofsomething useful for them and their treatment.

All of the above cited applications are owned by the assignee of thepresent application and herein incorporated by reference in theirentireties.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY

Systems and methods according to present principles meet the needs ofthe above in several ways. In particular, systems and methods accordingto present principles only alert users when it makes sense to do so,e.g., only alert the user when the system can predict or estimate thatthe user is not already cognitively aware of their current condition,e.g., particularly where the current condition is a diabetic statewarranting attention. In this way, the alert or alarm is personalizedand made particularly effective for that user. Such systems and methodsstill alert the user when action is necessary, e.g., a bolus ortemporary basal rate change, or provide a response to a missed bolus ora need for correction, but do not alert when action is unnecessary,e.g., if the user is already estimated or predicted to be cognitivelyaware of the diabetic state warranting attention, or if correctiveaction was already taken.

In a first aspect, a non-transitory computer readable medium isprovided, including instructions for causing a computing environment toperform a method of dynamically adjusting or tuning user alerts based ona cognitive awareness determination, thereby providing data relevant totreatment of a diabetic state warranting attention, the method includingsteps of: (a) identifying a current or future diabetic state warrantingattention, the identifying based at least partially on a glucoseconcentration value; (b) estimating or predicting a cognitive awarenessof the user of the identified current or future diabetic statewarranting attention; and (c) if the result of the estimating orpredicting is that the user is cognitively unaware of the identifiedcurrent or future diabetic state warranting attention, then alerting auser with a user prompt on a user interface of a monitoring device, theuser prompt indicating the diabetic state warranting attention; (d)whereby the user is only alerted of the diabetic state warrantingattention if and at a time that the user is unaware of the diabeticstate warranting attention and that the notification is effective forthe user.

Implementations of the aspects and embodiments may include one or moreof the following. The alerting may be optimized for cognitive awarenessof the patient such that fewer alarms occur than would otherwise beprovided without consideration of user cognitive awareness. Themonitoring device may be a smart phone, a smart watch, a dedicatedmonitoring device, or a tablet computer. In systems and methodsaccording to present principles, over-prompting, repeat prompts, ornuisance prompts are minimized or avoided. In systems and methodsaccording to present principles, the user is enabled to build trust thatthe system will only alert on notifications optimized or effective forthe user. The estimating or predicting a cognitive awareness of the usermay include determining if the identified current or future diabeticstate warranting attention includes an atypical glucose trace. Theatypical glucose trace may include an atypical pattern or an atypicalglucose response.

The estimating or predicting a cognitive awareness of the user mayinclude determining if the user has previously treated a like identifieddiabetic state warranting attention by taking an action without a userprompt. The action may be dosing of a medicament, eating a meal orexercising. The estimating or predicting a cognitive awareness of theuser may include determining if the user has entered meal or bolus data,or has requested a bolus calculation. The estimating or predicting acognitive awareness of the user may include determining if user behavioris consistent with cognitive awareness. The estimating or predicting acognitive awareness of the user may include receiving user input andbasing the estimating or predicting at least in part on the receivedinput. The estimating or predicting a cognitive awareness of the usermay include analyzing historic data of glucose values of the user versustime.

The steps of identifying and estimating or predicting are repeated untilsuch a time as the user is estimated or predicted to be cognitivelyunaware of the identified diabetic state warranting attention, and thenperforming a step of alerting the user with the user prompt. Theestimating or predicting a cognitive awareness of the user may includereceiving data from an application or website through an appropriateAPI. The estimating or predicting may be based at least partially onlocation data, namely GPS data. The location data may be that of theuser or that of a follower of the user.

The estimating or predicting a cognitive awareness of the user may bebased at least partially on one or more of the following: populationdata, data associated with behavioral or contextual information, dataassociated with a life goal of the user, data associated with a userprivacy setting, or a combination of these. The estimating or predictinga cognitive awareness of the user may be based at least partially onreal-time data, and where the real-time data may include one or more ofthe following: data associated with a GPS application in the monitoringdevice, data associated with an accelerometer in the monitoring device,data associated with behavioral or contextual information, dataassociated with a location of a follower of the user, data associatedwith a metabolic rate of the user, data associated with a glycemicurgency index of the user, heart rate data, sweat content data, dataassociated with a wearable sensor of the user, insulin data, or acombination of these.

The estimating or predicting a cognitive awareness of the user mayinclude recognizing one or more individualized patterns associated withthe user. The individualized pattern may correspond to an envelope ofcharacteristic analyte concentration signal traces occurring before orafter an event. The event may be associated with a meal, exercise, orsleep. The determination may be that the user is cognitively unaware ifa current signal trace falls outside the envelope of characteristicanalyte concentration signal traces.

The method further may include indicating a confidence level associatedwith the user prompt. If the result of the estimating or predicting isthat the user is not cognitively aware of the diabetic state warrantingattention, then the method may further include displaying the userprompt immediately. The estimating or predicting may further be based onlocation information of the user, where the location informationindicates that the user is within a predetermined threshold proximity ofa food store or restaurant. If the result of the estimating orpredicting is that the user is not cognitively aware of the diabeticstate that warrants attention, then the method may further includealerting the user with the user prompt after a time delay, a duration ofthe time delay based on at least the identified diabetic statewarranting attention and the glucose concentration value and/or aglucose concentration value rate of change.

The user prompt may include a query for a user to enter data. The querymay request data for the user to enter about dosing, meals or exercise.If the user ignores the user prompt as determined by data from the userinterface or data from an accelerometer associated with the monitoringdevice, and if the user prompt does not correspond to a dangercondition, then the method may further include storing information aboutthe user ignoring the user prompt under prior conditions and using thestored information as part of a subsequent estimating or predictingstep.

The identifying a current or future diabetic state warranting attentionmay include determining a clinical value of a glucose concentrationand/or a glucose rate of change and/or a glycemic urgency index value.The identifying a current or future diabetic state warranting attentionmay include measuring a glucose signal signature and comparing themeasured signature with a plurality of binned signatures, andclassifying the diabetic state warranting attention into one of aplurality of bins based on the comparison. The identifying a current orfuture diabetic state warranting attention may include determining oneor more time-based trends in the glucose concentration value, and basingthe identified state on the determined trend. The trend may correspondto whether the glucose concentration value is hovering within a range oris rising or falling, where hovering constitutes staying within apredetermined range for a period of greater than 5 or 10 or 15 or 30minutes. Fuzzy boundaries may be employed for defining the range.

The method may further include transmitting an indication of thediabetic state warranting attention to a medicament pump. If the resultof the estimating or predicting is that the user is not cognitivelyaware of the diabetic state warranting attention, then the method mayfurther include activating the medicament pump to provide a medicamentbolus. The medicament bolus may be a meal bolus of insulin. If theresult of the estimating or predicting is that the user is notcognitively aware of the diabetic state warranting attention, then themethod may further include activating the medicament pump to change abasal rate. The medicament may be insulin. The method may furtherinclude determining if the medicament pump can treat the diabetic statewarranting attention either fully or partially, and if so, then notalerting the user or altering the user prompt, respectively, as comparedto a case where the medicament pump cannot treat the diabetic state. Ifthe result of the estimating or predicting is that the user is notcognitively aware of the diabetic state warranting attention, then themethod may further include determining when to alert the user with theuser prompt. The user prompt, if displayed, may include a color or arrowinstead of or in addition to a glucose concentration value. The userprompt, if displayed, may include a prediction of a glucoseconcentration value. The user prompt, if displayed, may include anaudible indicator, and where the volume of the audible indicator isautomatically adjusted for ambient noise as measured by the monitoringdevice or by a device in signal communication with the monitoringdevice, where the adjusting for ambient noise may include raising thevolume of the audible indicator relative to the ambient noise until athreshold level of signal to noise ratio is achieved. The user promptmay be related to a diabetic state warranting attention occurring duringa period when the user has a glycemic urgency index that is low.

If the result of the estimating or predicting is that the user is notcognitively aware of the diabetic state warranting attention, then themethod may further include alerting the user with the user prompt aftera delay based not on a time duration but on the identified diabeticstate warranting attention and on the glucose concentration value and/ora glucose concentration value rate of change. If the result of theestimating or predicting is that the user is not cognitively aware ofthe diabetic state warranting attention, then the method may furtherinclude alerting the user with the user prompt after a delay based noton a time duration but on an individualized pattern learned by themonitoring device.

The identified diabetic state warranting attention may correspond to anatypical glucose response or an atypical pattern, where the atypicalresponse or atypical pattern is learned by a monitoring device and notby user entry. The user prompt may be displayed with dynamic timing on apredesigned user interface and not on an adaptive user interface. If theresult of the estimating or predicting is that the user is notcognitively aware of the diabetic state warranting attention, then themethod may further include alerting the user with the user promptimmediately regardless of indications to not alert the user with a userprompt received from other monitoring device applications. If the resultof the estimating or predicting is that the user is not cognitivelyaware of the diabetic state warranting attention, then the method mayfurther include alerting the user with the user prompt immediatelyregardless of indications to not alert the user with a user prompt basedon other user-entered data or settings. The estimating or predicting ifthe user is cognitively aware of the diabetic state warranting attentionmay be based at least in part on real-time data and not entirely onretrospective data.

In a second aspect, a system for providing smart alerts corresponding todiabetic states warranting user attention is provided, including: a CGMapplication running on a mobile device, the CGM application configuredto receive data from a sensor on an at least periodic or occasionalbasis and to calibrate and display glucose concentration data inclinical units; and a smart alerts application running as a subroutinewithin the CGM application or running as a parallel process with the CGMapplication on the mobile device and receiving data from the CGMapplication, the smart alerts application configured to perform themethod contained on the medium of claim

In a third aspect, a non-transitory computer readable medium isprovided, including instructions for causing a computing environment toperform a method of safely reducing alerting of users to diabetic statesthat require attention, the method including steps of: (a) identifying acurrent or future diabetic state warranting attention, the identifyingbased at least partially on a glucose concentration value; (b)determining if the identified diabetic state warranting attention isatypical for the user; (c) if a result of the determining is that theidentified diabetic state is atypical for the user, then alerting theuser with a user prompt on a user interface of a monitoring device, theuser prompt indicating the diabetic state warranting attention; (d)whereby the user is only notified of the diabetic state warrantingattention if the identified diabetic state is atypical for the user.

Implementations of the aspects and embodiments may include one or moreof the following. The determining if the identified diabetic statewarranting attention is atypical for the user may include determining ifthe identified diabetic state may include a glucose trace following apattern that is not typical of other patterns associated with the user.The determining if the identified diabetic state warranting attention isatypical for the user may include determining if the identified diabeticstate may include a glucose trace following a trend that is not typicalof other trends associated with the user.

In a fourth aspect, a non-transitory computer readable medium isprovided, including instructions for causing a computing environment toperform a method of prompting a user about a diabetic state thatwarrants attention, the computing environment in signal communicationwith a medicament delivery device, the user prompt optimized foreffectiveness to the user at least in part by being reduced in number,the user prompt providing data relevant to treatment of the diabeticstate warranting attention, the method including steps of: (a)identifying a current or future diabetic state warranting attention, theidentifying based at least partially on a glucose concentration value;(b) performing a first estimating or predicting of a cognitive awarenessof the user of the identified current or future diabetic statewarranting attention; (c) if the result of the first estimating orpredicting is that the user is cognitively unaware of the identifiedcurrent or future diabetic state warranting attention, then performing asecond estimating or predicting of a computer awareness of themedicament delivery device of the identified current or future diabeticstate warranting attention; (d) if the result of the second estimatingor predicting is that the medicament delivery device is unaware of theidentified current or future diabetic state warranting attention, thenalerting the user with a user prompt on a user interface of a monitoringdevice, the user prompt indicating the diabetic state warrantingattention; (e) whereby the user is only notified of the diabetic statewarranting attention if and at a time that both the user and themedicament delivery device are unaware of the diabetic state warrantingattention and that the notification is effective for the user.

Implementations of the aspects and embodiments may include one or moreof the following. The method may further include steps of determining ifthe medicament delivery device is capable of treating the identifiedcurrent or future diabetic state warranting attention, and if the resultof the determining is that the medicament delivery device is incapableof treating the identified diabetic state, then alerting the user withthe user prompt. The current or future diabetic state may includehypoglycemia, the medicament delivery device may be an insulin deliverydevice, and the method may further include shutting off or reducingactivity of the insulin delivery device based on the diabetic state ofhypoglycemia. The shutting off or reducing activity may occur sooner inthe case where the user is cognitively unaware of the hypoglycemia. Theperforming a first estimating or predicting may be based at leastpartially on user interaction with the medicament delivery device.

In further aspects and embodiments, the above method features of thevarious aspects are formulated in terms of a system as in variousaspects. Any of the features of an embodiment of any of the aspects,including but not limited to any embodiments of any of the first throughfourth aspects referred to above, is applicable to all other aspects andembodiments identified herein, including but not limited to anyembodiments of any of the first through fourth aspects referred toabove. Moreover, any of the features of an embodiment of the variousaspects, including but not limited to any embodiments of any of thefirst through fourth aspects referred to above, is independentlycombinable, partly or wholly with other embodiments described herein inany way, e.g., one, two, or three or more embodiments may be combinablein whole or in part. Further, any of the features of an embodiment ofthe various aspects, including but not limited to any embodiments of anyof the first through fourth aspects referred to above, may be madeoptional to other aspects or embodiments. Any aspect or embodiment of amethod can be performed by a system or apparatus of another aspect orembodiment, and any aspect or embodiment of a system or apparatus can beconfigured to perform a method of another aspect or embodiment,including but not limited to any embodiments of any of the first throughfourth aspects referred to above.

People with diabetes face many problems in controlling their glucosebecause of the complex interactions between food, insulin, exercise,stress, activity, and other physiological and environmental conditions.Established principles of management of glucose sometimes are notadequate because there is a significant amount of variability in howdifferent conditions impact different individuals and what actions mightbe effective for them, and as noted above, even providing alerts oralarms is problematic because different situations call for differentactions among different individuals. Providing alerts and alarmscustomized to the user and anticipatory of situations users may not becognitively aware of is thus highly beneficial and desirable to users.

Accordingly, systems and methods according to present principles providetechniques to alert and/or alarm users pertaining to diabetic situationsor states warranting their attention, but generally only when the systemcan determine, e.g., estimate or predict, that the user is not alreadycognitively aware of the situation. Consequently, such systems andmethods according to present principles reduce the uncertainty thatdiabetes is typically associated with and improve quality of life.

Advantages may include, in certain aspects or embodiments, one or moreof the following. “Smart alerts” are provided to advantageously notifyusers of diabetic states warranting attention, particularly where theuser is otherwise cognitively unaware of the diabetic state. Such smartalerts thus do not annoy the user as they only happen when needed, andnot when unnecessary, e.g., smart alert functionality would not alertthe user if the user doses a proper amount of insulin at a proper timerelative to a meal. Such smart alerts alert users of dangerous or urgentconditions more efficiently than in the case of prior art alerts, andprovide greater assurance and confidence. Such “smart” alerts furtheravoid the problems of alert fatigue. In particular, because CGM appsrunning on smart phones have not previously been able to quantify thecognitive state of a user, alert fatigue commonly occurs withthreshold-based alerting algorithms. Embodiments described herein infercognitive state data by converting physiological and non-physiologicaldata into an estimation or prediction of a user's cognitive state,providing for smarter alerts and reduced alert fatigue. Other advantageswill be understood from the description that follows, including thefigures and claims.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments now will be discussed in detail with an emphasison highlighting the advantageous features. These embodiments depict thenovel and non-obvious systems and methods according to presentprinciples, shown in the accompanying drawings, which are forillustrative purposes only. These drawings include the followingfigures, in which like numerals indicate like parts:

FIG. 1 is a schematic illustration of a system according to presentprinciples.

FIG. 2 is a flowchart of a first method according to present principles.

FIG. 3 illustrates schematically where a smart alerts application mayrun or be instantiated, according to present principles.

FIG. 4 is a flowchart of a second method according to presentprinciples.

FIG. 5 is a logical diagram of inputs to a smart alerts functionality orapplication, and a resulting smart alert output.

FIG. 6 is a flowchart of a third method according to present principles.

FIGS. 7 and 8 show a smart alert (FIG. 7) and a glucose trace atop whichthe smart alert appears.

FIGS. 9-14 illustrate additional implementations of smart alert outputson a user interface according to present principles.

FIGS. 15-17 illustrate yet additional implementations of smart alertoutputs on a user interface according to present principles.

FIGS. 18-21 illustrate additional implementations of smart alerts andrespective glucose trace charts on which the smart alerts are overlaid.

FIGS. 22-29 illustrate a time progression of smart alerts according topresent principles.

FIGS. 30-41 illustrate implementations of smart alerts as part of a lockscreen on a smart phone.

FIG. 42 is a schematic illustration of a system according to presentprinciples incorporating data from a delivery device.

FIG. 43 is a flowchart of a fourth method according to presentprinciples.

FIG. 44 is a flowchart of a fifth method according to presentprinciples.

FIG. 45 is a schematic illustration of a system according to presentprinciples.

FIG. 46 is a more detailed schematic illustration of a sensorelectronics module.

Like reference numerals refer to like elements throughout. Elements arenot to scale unless otherwise noted.

DETAILED DESCRIPTION

Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein generally relates to, withoutlimitation, a substance or chemical constituent in a biological fluid(for example, blood, interstitial fluid, cerebral spinal fluid, lymphfluid or urine) that can be analyzed. Analytes can include naturallyoccurring substances, artificial substances, metabolites, and/orreaction products. In some embodiments, the analyte for measurement bythe sensor heads, devices, and methods is glucose. However, otheranalytes are contemplated as well, including but not limited toacarboxyprothrombin; acylcarnitine; adenine phosphoribosyl transferase;adenosine deaminase; albumin; alpha-fetoprotein; amino acid profiles(arginine (Krebs cycle)), histidine/urocanic acid, homocysteine,phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine;arabinitol enantiomers; arginase; benzoylecgonine (cocaine);biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4;ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol;cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatinekinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine;de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylatorpolymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cysticfibrosis, Duchenne/Becker muscular dystrophy, analyte-6-phosphatedehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D,hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis Bvirus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD,RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol);desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanusantitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D;fatty acids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus,Dracunculusmedinensis, Echinococcusgranulosus, Entamoeba histolytica,enterovirus, Giardia duodenalisa, Helicobacter pylori, hepatitis Bvirus, herpes virus, HIV-1, IgE (atopic disease), influenza virus,Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacteriumleprae, Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus,parainfluenza virus, Plasmodium falciparum, poliovirus, Pseudomonasaeruginosa, respiratory syncytial virus, rickettsia (scrub typhus),Schistosoma mansoni, Toxoplasma gondii, Trepenomapallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereriabancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The term “calibration” as used herein generally relates withoutlimitation to the process of determining the relationship between sensordata and corresponding reference data, which can be used to convertsensor data into meaningful values substantially equivalent to thereference data, with or without utilizing reference data in real time.In some embodiments, namely, in continuous analyte sensors, calibrationcan be updated or recalibrated (at the factory, in real time and/orretrospectively) over time as changes in the relationship between thesensor data and reference data occur, for example, due to changes insensitivity, baseline, transport, metabolism, and the like.

The terms “calibrated data” and “calibrated data stream” as used hereingenerally relate without limitation to data that has been transformedfrom its raw state (e.g., digital or analog) to another state using afunction, for example a conversion function, to provide a meaningfulvalue to a user.

The term “algorithm” as used herein generally relates without limitationto a computational process (for example, programs) involved intransforming information from one state to another, for example, byusing computer processing. In implementations described here, algorithmsmay implement decision-support application/functionality, which takesinput from sensors, computer applications, or user input, and convertsthe same into outputs rendered to a user on a user interface or to otherdevices.

The term “sensor” as used herein generally relates without limitation tothe component or region of a device by which an analyte can bequantified.

The terms “glucose sensor” generally relates without limitation to anymechanism (e.g., enzymatic or non-enzymatic) by which glucose can bequantified. For example, some embodiments utilize a membrane thatcontains glucose oxidase that catalyzes the conversion of oxygen andglucose to hydrogen peroxide and gluconate, as illustrated by thefollowing chemical reaction:Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used hereingenerally relate without limitation to one or more components beinglinked to another component(s) in a manner that allows transmission ofsignals between the components. For example, one or more electrodes canbe used to detect the amount of glucose in a sample and to convert thatinformation into a signal, e.g., an electrical or electromagneticsignal; the signal can then be transmitted to an electronic circuit. Inthis case, the electrode is “operably linked” to the electroniccircuitry. These terms are broad enough to include wirelessconnectivity.

The term “variation” as used herein generally relates without limitationto a divergence or amount of change from a point, line, or set of data.In one embodiment, estimated analyte values can have a variationincluding a range of values outside of the estimated analyte values thatrepresent a range of possibilities based on known physiologicalpatterns, for example.

The terms “physiological parameters” and “physiological boundaries” asused herein generally relate without limitation to the parametersobtained from continuous studies of physiological data in humans and/oranimals. For example, a maximal sustained rate of change of glucose inhumans of about 4 to 5 mg/dL/min and a maximum acceleration of the rateof change of about 0.1 to 0.2 mg/dL/min² are deemed physiologicallyfeasible limits; values outside of these limits would be considerednon-physiological. As another example, the rate of change of glucose islowest at the maxima and minima of the daily glucose range, which arethe areas of greatest risk in patient treatment, and thus aphysiologically feasible rate of change can be set at the maxima andminima based on continuous studies of glucose data. As a furtherexample, it has been observed that the best solution for the shape ofthe curve at any point along a glucose signal data stream over a certaintime period (for example, about 20 to 30 minutes) is a straight line,which can be used to set physiological limits. These terms are broadenough to include physiological parameters for any analyte.

The term “measured analyte values” as used herein generally relateswithout limitation to an analyte value or set of analyte values for atime period for which analyte data has been measured by an analytesensor. The term is broad enough to include data from the analyte sensorbefore or after data processing in the sensor and/or receiver (forexample, data smoothing, calibration, and the like).

The term “estimated analyte values” as used herein generally relateswithout limitation to an analyte value or set of analyte values, whichhave been algorithmically extrapolated from measured analyte values.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

The phrase “continuous glucose sensor” as used herein generally relateswithout limitation to a device that continuously or continually measuresthe glucose concentration of a bodily fluid (e.g., blood, plasma,interstitial fluid and the like), for example, at time intervals rangingfrom fractions of a second up to, for example, 1, 2, or 5 minutes, orlonger.

The phrases “continuous glucose sensing” or “continuous glucosemonitoring” as used herein generally relate without limitation to theperiod in which monitoring of the glucose concentration of a host'sbodily fluid (e.g., blood, serum, plasma, extracellular fluid, tearsetc.) is continuously or continually performed, for example, at timeintervals ranging from fractions of a second up to, for example, 1, 2,or 5 minutes, or longer. In one exemplary embodiment, the glucoseconcentration of a host's extracellular fluid is measured every 1, 2, 5,10, 20, 30, 40, 50 or 60 seconds.

The term “substantially” as used herein generally relates withoutlimitation to being largely but not necessarily wholly that which isspecified, which may include an amount greater than 50 percent, anamount greater than 60 percent, an amount greater than 70 percent, anamount greater than 80 percent, an amount greater than 90 percent, ormore.

The terms “processor” and “processor module,” as used herein generallyrelate without limitation to a computer system, state machine,processor, or the like, designed to perform arithmetic or logicoperations using logic circuitry that responds to and processes thebasic instructions that drive a computer. In some embodiments, the termscan include ROM and/or RAM associated therewith.

The terms “decision-support application” and “decision-supportapplication/functionality,” as used herein generally relate withoutlimitation to algorithms that use sensor data and/or other data, e.g.,user-entered data, derived data, or data from other applications orsensors, to provide a user prompt on a display and/or a command to amechanical device.

The term “distinct” in regard to variables or parameters as used hereingenerally relate without limitation to a parameters and/or variablesthat are independent and do not rely one upon the other. Conversely, theterm “related” in regard to variables or parameters as used hereingenerally relates without limitation to parameters and/or variables thatdepend in some way on each other or are derivable from each other. Forexample, a sensor signal time derivative is related to a sensor signal,while a user's gender and current analyte concentration would beconsidered distinct. It is noted here, however, that multiple parametersused in decision-support application/functionality may involve a singleact or event, e.g., the same may concern a timing and duration ofexercise. The term “independent” is used in the same way as “distinct”,and similarly “dependent” is used in the same way as “related”, although“independent” may also refer to variables used in a function, whereinthe output of the function is a “dependent” variable, with thedependency based on the underlying independent variables.

The term “insulin sensitivity” as used herein generally relates withoutlimitation to the relationship between how much insulin needs to beproduced in order to deposit a certain amount of glucose. It is aphysiologic measure everyone has, and is not limited to diabetes. Thesame may vary throughout a person's day, e.g., and may be driven byhormones, activity, and diet. It may further vary throughout a person'slife, and may be driven by, e.g., illness, weight, obesity, and so on.It is a general measure, e.g., like weight, blood pressure, heart rate,and so on. There are healthy ranges for insulin sensitivity, as well asunhealthy ranges. In diabetes management, users should generally knowtheir insulin sensitivity factor (ISF) when making decisions aboutboluses. The term ISF is sometimes used interchangeably with “correctionfactor” (CF). For example, a typical calculation users may be requiredto perform may be, e.g., “if my blood glucose is too high by 100 mg/dL,how many units of insulin do I need to take to correct the high andbring my blood glucose down by that 100 mg/dL?” Many users use a defaultCF of 1:50, which means that one unit of insulin will reduce bloodglucose by 50 mg/dL. The determination of insulin sensitivity, as wellas the determination of other types of sensitivities, are discussed ingreater detail below, but here it will be noted that knowledge ofinsulin sensitivity may be based on, e.g., real-time analysis of datafrom CGM, activity monitors, and insulin pump data, as well as on datafrom retrospective analysis of such sensors as well as data from, e.g.,an electronic health record. Other factors that may bear on insulinsensitivity or ISF may include correlations with time of day, pain,and/or exercise; heart rate variability, stroke volume, othercardiovascular health related to metabolic issues; ability to distributeinsulin; temperature; insulin type, based on insulin sensitivitymeasurements, profiles, peaks, time between peaks; atmospheric pressure(thus “airplane mode” may be an input); whatever activity affects thepatient or user the most; and so on.

The term “insulin resistance” as used herein generally relates withoutlimitation to a medical condition in which the cells in a person's bodycannot make proper use of insulin for the normal processes of cellsimporting glucose, or other metabolites, from the bloodstream. Insulinresistance reduces insulin sensitivity. While everyone has an insulinsensitivity, only certain individuals suffer from having insulinresistance.

The term “lifestyle factors” generally refers without limitation toquantitative or qualitative (but in some way convertible toquantitative) parameters that are generally not measured directly with aphysiological sensor but which are related to disease management. Insome cases the same relates to a trend or recurring event, howeverminor, that systems and methods according to present principles maydetermine and use in providing a therapy prompt to a user or in changingor altering a therapy prompt to a user. However, trend information neednot necessarily correspond to a pattern, although some patterns willconstitute trend information. In some implementations, a lifestylefactor may be equated to the correlative parameter discussed elsewhere.Lifestyle factors (also termed “lifestyle context”) may be related tocertain quantities that are physiological, e.g., insulin sensitivity,but may also be related to more external parameters, such as sleepsensitivity, meal sensitivity, exercise sensitivity, and so on. In otherwords, lifestyle factors are generally quantitatively determinable, butin most cases are not directly measured by a sensor.

The term “state” and “state model” generally refer without limitation toa data structure useful for modeling a patient for purposes of, e.g.,decision-support or smart alerts. Generally a state model of a patientenvisions the patient as occupying one of a plurality of states, thestates dependent on various lifestyle factors and clinical factors. As aspecific example, the state of a patient may correspond to a currentinsulin sensitivity profile. The plurality of states or state model maythen be employed in combination with a real-time input, e.g., time,calendar, CGM glucose value, rate of change, and the like, in order toprovide a therapy prompt to the user supporting a therapeutic decision.In one implementation a number of diabetes decision states are definedby one or more highly correlative parameters, which can be lifestyleparameters, and which can be selected by the user through a userinterface or learned over time via machine learning and/or cloudanalytics.

The term “diabetic state warranting attention” generally refers to abiological state of a diabetic user in which it is desirable that anaction be taken. For example, a condition of hypoglycemia orhyperglycemia is a diabetic state warranting attention. Where suchconditions are impending or likely to occur, but has not happened yet,the user is also considered to be in a diabetic state warrantingattention. Diabetic states warranting attention may vary in urgency, butgenerally refer to user conditions in which an action is estimable ordeterminable and is beneficial to the user, generally leading the usertowards a condition of euglycemia or towards a center of a target rangeof glucose values, e.g., a target range corresponding to euglycemia.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of another analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, non-invasive, intraocularand/or intravascular (e.g., intravenous) device. In some embodiments,the device can analyze a plurality of intermittent blood samples. Theglucose sensor can use any method of glucose measurement, includingenzymatic, chemical, physical, electrochemical, optical, optochemical,fluorescence-based, spectrophotometric, spectroscopic (e.g., opticalabsorption spectroscopy, Raman spectroscopy, etc.), polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (HCP, e.g., doctor, physician, nurse,caregiver), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor capable of measuring the concentration of glucose in a host, thesystems and methods of embodiments can be applied to any measurableanalyte. Some exemplary embodiments described below utilize animplantable glucose sensor. However, it should be understood that thedevices and methods described herein can be applied to any devicecapable of detecting a concentration of analyte and providing an outputsignal that represents the concentration of the analyte.

In some embodiments, the analyte sensor is an implantable glucosesensor, such as described with reference to U.S. Pat. No. 6,001,067 andU.S. Patent Publication No. US-2011/0027127. In some embodiments, theanalyte sensor is a transcutaneous glucose sensor, such as describedwith reference to U.S. Patent Publication No. US-2006-0020187-A1. In yetother embodiments, the analyte sensor is a dual electrode analytesensor, such as described with reference to U.S. Patent Publication No.US-2009/0137887-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007/0027385-A1. Thesepatents and publications are incorporated herein by reference in theirentirety.

The following description and examples describe the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

Systems and methods according the present principles provide ways toincorporate “smart alerts” in analyte monitoring systems, and inparticular continuous glucose monitoring systems. In one implementation,smart alerts can be provided by its own application or algorithm, whichgenerally runs alongside a CGM application. In another implementation,smart alerts can be implemented by additional programming/instructionsadded to an existing application, e.g., a CGM application. For thisreason, in this specification, the smart alerts provided are generallyreferred to as smart alerts application/functionality.

Certain exemplary aspects are shown in FIG. 1. In the figure, a system50 is illustrated in which a patient 102 wears a sensor 10, and thesensor transmits measurements using sensor electronics 12. The sensorelectronics may transmit data corresponding to analyte measurements to asmart device 18, e.g., a smart phone and/or smart watch, to a dedicatedreceiver 16, or to other devices, e.g., laptops, insulin deliverydevices or other computing environments. Currently measured data,historical data, analysis, and so on, may be transmitted to a server 115and/or a follower device 114′. Such data may also be transmitted to ahealthcare professional (HCP) device 117. More detailed aspects of thesensor itself and sensor electronics are described below with respect toFIGS. 45 and 46.

Referring to the flowchart 101 of FIG. 2, a method according to animplementation of present principles is seen for enabling smart alertfunctionality within an analyte monitoring system, e.g., within acontinuous glucose monitor. A first step is a determination as towhether the user is in a diabetic state that requires or warrantsattention (step 13). This step is generally performed in mostimplementations as, if the diabetic state does not warrant attention, asmart alert need not be given (step 11). As noted, however, even if theuser is in a diabetic state that warrants attention, an alert is notalways given.

In particular, an estimation or prediction is made by the system usingvarious data, which may be past date and/or real time data, and whichmay be from a sensor and/or other measurement devices, as to whether theuser is cognitively aware of the diabetic state that warrants attention(step 17). If the result of the estimation or prediction is that theuser is cognitively aware, then again a determination will be to notalert (step 11). However, if the result of the estimation or predictionis that the user is not cognitively aware, then a smart alert may beprovided (step 19).

Generally the estimation or prediction will be done in an automaticmanner, and will be based on stored data, or current data received ordetermined. While various types of input data will be described below,here it is noted that such data may relate to data having the signatureof a pattern (if the user has experienced the pattern many times before,it may be assumed that they are cognitively aware), behavioral data,historical data (including the use of retrospective analyses in someimplementations), and so on. The result of the estimation or predictionmay be a binary yes/no, but in many other cases will be in the nature ofa quantitative estimation or prediction, e.g., with a percentagelikelihood that the user is cognitively aware. Of course, by comparingthe percentage with a single threshold, the same may be transformed intoa yes/no response. In various other cases, however, particularly wheremultiple thresholds are involved, multiple and different responses mayresult depending on the value of the percentage likelihood.

Other alternative implementations may include the use of user input. Forexample, by the use of slider bars, a selection of a choice of radiobuttons, or other user interface mechanisms, users may affect theoperation of the smart alerts functionality. Users may also affect thesensitivity of the functionality, depending on their desire forreminders and alerts. Users may further affect the content of thereminders, by selecting what information they would like to review uponthe occurrence of one or more categories of alerts/events. Via asuitable selection, users can thus affect the operation, timing, anddisplay of smart alerts.

Details of the above-described functionality are now described.

Referring to FIG. 3, the smart alerts functionality may operate as asecondary application 25, running alongside a primary analyte monitoringapplication, e.g., a primary CGM app, on a smart device 18, or the samemay be provided as functionality within the CGM app (or another app)running on the smart device 18. In either case, other functionality maybe implemented as part of such a secondary application or functionality.Implemented as a secondary application running alongside a primarymonitoring application, additional or subsequent update functionalitymay be tested without affecting the functionality of the main CGM app.Generally, the smart alerts functionality provides technologicalimprovements to the operating of the monitoring application, as feweralerts are usually needed, which is less expensive computationally,saves on battery power, and so on. In addition, the device itself isprovided with technological functionality related to data that priorsystems lacked, e.g., data about cognitive awareness of the user oftheir diabetic state.

Referring next to the chart 200 of FIG. 4, additional details areprovided for step 17, that is, for the system to utilize machinelearning and stored and/or real-time data to determine whether the useris cognitively aware of the diabetic state that warrants attention. Asnoted above, in some implementations this step is potentially phrased asconstituting machine or system learning for prediction or estimation ofthe user cognitive awareness of the diabetic state that warrantsattention (step 22). That is, the system determines metrics that areused in machine learning but also used in real time to obtain ordetermine data that is in some way compared to other data, e.g.,compared to a threshold, to provide a prediction or estimation as to theuser cognitive awareness, and the same then employed in a determinationto provide a smart alert.

One way the system can determine such metrics allowing prediction orestimation of user cognitive awareness of such a diabetic state is bydetermining data about if the diabetic state, or put another way thephysiological glucose response experienced, is typical of datapreviously seen and/or experienced by the user (step 24). If the machinelearning learns what is typical data for the user, and if metricsobtained or determined indicate that current real-time data is similarto such typical data, then in many cases no alert need be given, as thesystem determination may be that the user is already cognitively aware,i.e., the estimation or prediction results in a high likelihood that theuser is aware of their diabetic state that warrants attention. Suchsimilarity in data may be determined in a number of ways, includingdetermining if a real-time current glucose trace has a characteristicsignature that is similar to a previously determined characteristicsignature, e.g., duration, rise time, width, FWHM, and so on.Conversely, if the physiological response is atypical for the user, thenthere is a correspondingly lessened quantitative likelihood that theuser has such cognitive awareness, and in this case a smart alert may begenerated based on the data of the lessened quantitative likelihood, thesmart alert resulting in a rendering on a screen or display of anindication of the diabetic state warranting attention, where it isunderstood that such rendering results in an alteration of the userinterface portrayed on such a screen or display. For example, thephysiological response may include a series of measured glucose valueswith respect to time. If the series of measured glucose values issimilar to prior series of measured glucose values encountered, e.g.,over the same or a similar time period, e.g., a quantified similarity isgreater than a predetermined threshold criterion, then such similarityincreases the likelihood that the user is aware of their diabetic statethat warrants attention.

In a particular implementation, it may be determined if thephysiological response is part of an established pattern for the user(step 26). Here the term “pattern” is used to relate to a repeating dataarrangement identified in received data, e.g., in glucose monitoring, anoccurrence of “overnight lows” that commonly occurs with the user. Ifthe physiological response is part of a pattern that the user hasencountered before, then again the estimation or prediction oflikelihood of user cognition may be high, or in a more quantitativeand/or granular calculation, may be raised or heightened. The degree ofheightening or raising may be based at least in part on the frequency ornumber of times the user has experienced the pattern previously. Theidentification of an established pattern may include the followingsteps, which generally pertain to a series of measured glucose valueswith respect to time. The identifying may include: quantifying asimilarity in the received data over two or more periods of time, and ifthe quantified similarity is greater than a predetermined thresholdcriterion, then identifying the similarity as an established pattern.Typical identified patterns may include overnight lows, post-meal highs,post-meal lows, time of day highs, time of day lows, weekend versusweekday highs/lows, post event highs/lows, and best days. To the extentthese identified patterns occur in a given patient, smart alertsfunctionality may be configured to not cause an alert to be given to thepatient, as there is a high likelihood of cognitive awareness. It isfurther noted that in many cases events precede physiological responses,and such events may be detected and identified as being common to and/orpreceding the repeating data arrangement constituting the detectedpattern, e.g., having appeared in two or more data arrangements, halfthe data arrangements, 75% of the data arrangements, or the like. Inthis sense the term “common” is used to refer to appearing in more thanone data arrangement constituting a pattern, and not necessarily commonto the user in general. The prevalence of the event may be measured withreference to a predetermined ratio or percentage, such as appearing inat least 25% of the data arrangements constituting the pattern, 50%,75%, 90%, 95%, 99%, and so on. To the extent an alert is to bepredicated on the occurrence of an event, and to the extent the alertwould include recitation of the event as a cause, smart alertsfunctionality may further be configured to suppress or not alert on theparticular event, as again the user may be estimated or predicted to becognitively aware of the event.

In a specific implementation, it is noted that past systems predicatedthe issuance of an alert based on the passing of a threshold glucosealert, before an alert would be sounded that a user was going out ofrange. Predictive algorithms have been employed to develop predicteddata using a prediction algorithm which is in turn compared to athreshold to provide users advance warning that they are going out ofrange. In both cases, however, the system tolls the issuance of an alertuntil some degree of present or expected glucose excursion has occurred.

In systems and methods according to present principles, however, pastdata, as well as current real-time data corresponding to glucoseresponses to events such as exercising or eating, may be leveraged viamachine learning to identify a typical response for users. When the useris having a typical response, the system may suppress the issuance of analert or the system may never generate the alert in the first place.

However, when the systems or methods identify that a current glucosetrace is not typical as compared to prior glucose traces, i.e., the useris having an atypical response, users are alerted to take appropriateaction. For example, a user may, at lunch, be determined to haveatypical glucose response of a rise at 2 mg/dL to a high of 160-220mg/dL within one hour. If the smart alerts functionality identifies anatypical response, e.g., such as a glucose trace showing a rise of 3mg/dL or achieving a range of above 160 mg/dL within 30 minutes, thenthe system can base a smart alert on the atypical trace, causing arendering of an indication of the smart alert on a screen or displaying,alerting the user of a likely high glucose. Importantly, such anotification is not based just on a glucose trace passing a threshold ora predicted glucose value as in prior systems, but rather on the glucoseresponse being atypical or abnormal and thus likely leading to auniquely high glucose level after this particular lunch. In this way,the smart alerts functionality operates in a unique and very differentway than prior systems.

A particular benefit of this implementation is that the user is notbeing informed simply that their glucose is out of range or will soon beout of range, but rather the user is being informed of additionalinformation, i.e., that their glucose response is not typical for them.That is, based on the determined or obtained data, a unique andcustomized smart alert notification is portrayed on a user interfacerendered on a display or screen, displaying data of a type that has notbeen displayed before and further has not even been calculated before.Such notification allows the user to take additional precautions,unknown using the technology of prior systems, to manage and treat thismore unique scenario.

Referring next to the flowchart 250 of FIG. 5, the smart alertsfunctionality 28, which may be implemented by appropriate subroutines ormodules, and which may operate alongside a monitoring application orprovide functionality within a monitoring application, accepts varioustypes of inputs data 30 and, depending upon the input data, generates asmart alert 32 in response thereto. In so doing, the smart alertsfunctionality or module may perform relatively continuous evaluations.That is, generally the application does not just delay the providing ofan alert by a predetermined time so as to render the same moreconvenient for the user. Rather, the application continuously evaluatesdata inputs 30 and determines, based on the data, or based on otherdata, when and if to generate an alert. For example, if the system andmethod learn that a particular user generally emerges from a “high afterlunch” (postprandial high) 45 minutes after eating, then no alert willbe generated so long as the actually observed glucose trace, indicatingthe high, is consistent with that pattern or typical user response. Ifthe physiological response becomes inconsistent with the pattern ortypical user response, e.g., data of the measured and calibrated glucosedata trace from the glucose sensor differs substantially from typicalglucose data traces, e.g., is different by more than a predeterminedthreshold, thus making the response atypical, then the system willestimate or predict that the user is not cognitively aware, and a smartalert will be generated and an indication rendered on a screen ordisplay. Individualized or personalized user information/data isgenerally employed in the determination of whether (or when) to generatethe smart alert, particularly as the same is generally transformed intodata usable for an estimation or prediction of user cognitive awareness,and data of the estimation or prediction of cognitive awareness maysubsequently be employed in the determination of the timing of thealert, the way in which the alert is provided, the content of the alert,the format of the alert, and so on. In general the generation of thesmart alert, and/or its content or other features, are personalized ordynamically adapted or tuned to the user.

Individual inputs are described below, and the same may include datareceived as measured by signals, calibrated data, data from a userinterface of a monitoring device (including dedicated devices and smartphones/watches), e.g., data about keystrokes, taps, frequency ofinteraction, apps used, and so on. Here it is noted that the mechanicsof the estimation or prediction of cognitive awareness, i.e., how thesmart alerts functionality is performed, can generally includecomparison to a criterion (step 34). For example, a criterion mayinclude a known pattern, and a glucose trace, e.g., a physiologicalresponse corresponding to a diabetic state warranting attention, may becompared to the known pattern (criterion) in the determination ofwhether the physiological response is typical. For example, a similaritymay be gauged in shape, rise slope/time, duration, time of day, day ofweek, and so on.

Inputs

Analyte Concentration

Various metrics may be employed in the building of appropriatealgorithms to operate such smart alerts functionality, such metricsindicative either individually or in combination to derive an estimationor prediction of cognitive awareness of a diabetic state warrantingattention. In some cases such metrics are transformed into theestimation or prediction data, and in other cases an algorithm is usedto derive the estimation or prediction data. Such metrics include rateof change, time to a threshold glucose level (e.g., how fast theirglucose changes the first 20 mg/dL), insulin on board, and so on. Aprimary driver in smart alerts functionality is a real-time analyteconcentration value, e.g., glucose value, measured by a glucose sensor,as well as glucose rate of change, derived from the glucose valuemeasured by the sensor. However, other physiological quantities may alsobe employed. In some cases reception of data/calibration of data/displayof data is performed by a single physical device, while in other casesmultiple devices may be used, and in such cases data may be transmittedfrom one device to another as needed and according to appropriate datatransmission protocols.

Besides quantities measured or derived from glucose sensor data, anotherquantity that may be employed is a glycemic urgency index, as describedin US Patent Publication No. US-2014/0289821, filed Mar. 16, 2015, andentitled GLYCEMIC URGENCY ASSESSMENT AND ALERTS INTERFACE], owned by theassignee of the present application and herein incorporated by referencein its entirety.

User Input Or Behavior

User input or behavior, as gleaned by data input to systems according topresent principles, can also be employed in the estimation or predictionof cognitive awareness. For example, user behavior can indicatecognitive awareness as certain user behavior is consistent with userself-treatment of diabetic states warranting attention.

In one specific example, machine learning combined with system data ofuser interface usage may be employed to learn that the user responds toa first alert but rarely or never to subsequent ones. Thus, in thisexample, a first alarm may be made more evident, as it is known thatfuture alarms will be ignored.

User “clicks” or icon “activations”, as determined from user interfaceusage data, may further be employed to determine cognitive awareness.For example, if glucose trace data indicates that a user has entered adiabetic state warranting attention, but if the user immediately startschecking their monitoring app, e.g., calculating a bolus or rescue carbamount, then such activity strongly indicates the user is cognitivelyaware of their diabetic state, i.e., tending to raise the likelihood ofcognitive awareness higher in a quantitative estimation or prediction.In such cases smart alerts may be suppressed or never generated.

In the same way, entering certain types of data may be used in theestimation or prediction algorithm. For example, if a user has adiabetic state warranting attention of hypoglycemia, but the user enterscurrent meal data, then the user-entered data indicates cognitiveawareness of the hypoglycemic state, and thus would cause suppression ofa smart alert, or the lack of generation thereof. In some cases, whereit is a “closer call”, such a calculation may involve converting theuser entered data to carbohydrate data in order to determine if the useris intending to treat a low or is simply eating without such awareness.The same is true of entering bolus data in response to hyperglycemia,and so on. Entering data (by the user) for a bolus calculation mayfurther indicate user cognitive awareness, as can entering data (by theuser) setting parameters for the user interface, e.g., manipulation ortuning of slider bars. The value of the parameters themselves, e.g., lowaggressiveness, medium aggressiveness, high aggressiveness, maythemselves be used as separate inputs into smart alert functionality.Thus, in these implementations, relevant data includes: (1) the entry ofdata into an application pertaining to health and diabetes management,along with (2) the value of the data itself.

Entry of meal data may cause other variations in processing, which canfurther affect the generation or suppression of smart alerts. In someimplementations, these aspects serve as an incentive for the logging ofinsulin and carbohydrates. For example, if a user logs a significantamount of carbs, the monitoring application may automatically raise analert threshold level by a predetermined amount for a predeterminedduration of time, e.g., may automatically raise a high alert level by100 mg/dL for 2 hours. In this way, the same “desensitizes” the highalert level for the postprandial meal spike. Put another way, instead ofbasing an alert based on cognitive awareness of the diabetic statewarranting attention, this aspect modifies the system definition of thediabetic state warranting attention. In implementations, the amount ofalert level increase can be configurable, e.g., from 0 to 200 mg/dL in25 mg/dL increments, with the default level being 100 mg/dL. Where thelevel increase is 0, such essentially turns off the feature. Theduration of time may be configurable from, e.g., 30 minutes to 3 hoursin 15 minute increments, with the default period of time of two hours.The threshold level of carbs at which this desensitization subroutine isinitiated may vary, but the same may be, e.g., 2 or 3 carb units. Itwill be understood that such generally depends on insulin/carb ratio. Ifknown, parameters such as insulin on board and carbs on board may betaken into account in the desensitization subroutine. Benefits of suchdesensitization subroutines are many-fold, including that only onesetting screen is added, and that if the default values are applicable,no initial setup is required. If the user has a connected pump and theinsulin/carb ratio data is communicated using an appropriatetransmission protocol from the pump's bolus calculator, then the set-upis automatic and no additional data entry is required. System or machinelearning may still be advantageously employed in the implementation ofthis feature as well, as machine learning may be performed to determinewhen the user typically enters meal data vis-a-vis when the meal data isactually consumed, e.g., following consumption of the carbs, beforeconsumption of the carbs, and so on. Such logging, when combined withglucose data indicating a potential hypoglycemic situation, may beanalyzed and used to suppress the generation of a smart alert, whichalert may be unnecessary given the expected postprandial rise.

Other relevant data may include user entered content data, which may bedata entered on a form or using multiple-choice radio buttons or thelike. For example, users may be requested to directly comment on theusefulness of a given alert. The user could be prompted to acknowledgethe alert by depressing a button selected from a convenient andeasy-to-understand user interface. Buttons may be provided such as“THANK YOU” or “GO AWAY”. Responses such as these can allow the user torapidly acknowledge the alert and yet still be transformed into highlyuseful data for future calculations in smart alerts functionality. Forexample, if an alert was provided two hours after a meal, but was notedby the user as being not helpful, a next iteration may have the userbeing alerted 2.5 hours after a meal (and if other alert criteria aremet). As another example, if an alert is expressly noted as not helpful,the alert will not be repeated (i.e., defining criteria for smart alertsdeterminations in the future). If an alert is ignored, it may be notrepeated if other data can be used to indicate user awareness of thediabetic state, in which case the alert may be determined to be beingpurposely ignored. If it is not clear if the ignoration is purposeful,the alert may be repeated. The system and method can also determine databased on machine learning from either purposeful ignoration or by useractivation of an “ignore” button. For example, the system and method mayalert a user at 180 mg/dL (and rising) multiple times with no responseor with an activation of an “ignore” button. In such a situation, themonitoring app employing smart alerts functionality may ask the user ifthey do not desire this level of alerting, e.g., if they do not wish tobe alerted again in a similar situation. Such data may then cause achange in how such smart alerts are output, i.e., will cause additionaltuning or personalization to the user. In other words, such data maythen be employed in algorithms optimizing the generation of smart alertsby applying calculations that take into account user interaction data(as determined by user interface interactions) or othernon-physiological data as well as physiological data. Such algorithmsmay be operated on a smart phone type device as well as on otherdevices, e.g., smart watches.

Other relevant user entered data may include event data, e.g., if theuser is about to perform or take part in an event that may bear on theirglucose value. For example, if the user is about to exercise, e.g., do along workout, where they know their glucose will be outside of theirnormal range, the user may activate a setting on their monitor, e.g.,click a button on their smart phone, to activate a special “work out”alert schedule. Such a workout alert schedule may provide differentalert values for the duration of the event. Other such events whichcould have special alert schedules may include meals, sleep, or thelike.

Data pertaining to feedback from the user can be received from a userinterface at various times during the resolution of a diabetic statewarranting attention, e.g., during the event, well after the event, andso on.

Prompts or other questions inviting user response may be provided atvarious times so as to learn directly about user cognitive awareness orto learn about “markers” that indicate user cognitive awareness. In moredetail, prompts or other invitations for user interactions, particularlywith regard to entering data, may be provided to glean specific neededdata, i.e., dated determined to be particularly useful in determininguser cognition. Such data may be specific to a user or to a group ofusers, e.g., a user's cohort, or to a larger population. Put anotherway, the system using machine learning may prompt a user to enter aspecific type of data so that the system receives data determined to beparticularly useful. Such received or transmitted data pertains not justto the existence of user interaction but to the actual content and valueof the user interaction.

Besides use of directly-entered user information, inferred userinformation may also be employed in smart alerts functionality. Forexample, a user's lack of action can be used. For example, if a user isat 40 mg/dL and has not performed any actions in an hour, alerts canbecome more pervasive. This situation may be detected by a firmware orsoftware routine that is configured to measure the amount of time a useris in a dangerous or undesirable range and which has, as an additionalinput, keystroke or tap data from a user interface. As another example,if the user begins checking their display device with a high degree ofinteraction, then alerts may become more active, interactive, oraggressive, as the system can learn that, at that point in time, theuser is in a mode where they desire a significant amount of interactionand information. Similarly, data from a user interface may be employedto measure what a “significant” amount of user interaction is, relativeto a “normal” or “typical” amount. For example, a normal or typicalamount may be determined by user input data over time, e.g., via anaverage number of apps opened or taps per minute or per hour. Thatnumber may be employed as the basis for a threshold, and once many moresuch taps are measured or detected, a user “high interaction” mode maybe defined and used. Put another way, increased user interaction withthe device can have a similar effect as a user moving a slider bar in asetting parameter to a more aggressive state. In part such automaticsetting of a parameter may depend on to what extent the user interactionindicates user cognitive awareness. User interaction may be unrelated toa diabetic state warranting attention, e.g., if a user is responding toemail or watching videos. Thus, user interaction may be discriminated inthat only interactions related to analyte monitoring are considered,e.g., CGM, bolus calculation, and so on. Haphazard and franticinteraction of such apps may indicate user cognitive unawareness and adesire for interaction. The measurement of haphazard and franticinteraction may in some implementations take into account accelerometerdata, e.g., where the device is being handled in a frantic way. Thesmart alert would be generated in this instance. User-focusedinteraction, e.g., a deliberate and “typical” performing of a boluscalculation, particularly with a keystroke or tap frequency that isusual for the user, e.g., within a range that is deemed “acceptable” or“usual” or “typical”, would on the other hand indicate user cognitiveawareness, and thus would contraindicate the generation of a smartalert. A complete lack of accelerometer signal variation may indicatethat a user has fallen or has passed out. In this case, if an alert isnot acknowledged or a lack of movement continues, smart alertfunctionality may be configured to send an alert to a follower or toanother caregiver associated with the user. In general, any userinteraction determinable or measurable in real time from the userinterface of a device running an application related to health may beused in the determination of when and/or whether to alter a userinterface using the application, e.g., to provide an alert, particularlywhen used in combination with real time glucose data, and such userinteraction is defined as actions not only taken by a user on the userinterface but also actions not taken by a user.

Prior or historical user responses (either physiological or through auser interface) may be employed to develop, generate, or refine futuresmart alerts functionality, e.g., to eliminate or reduce user “yo-yo”responses, or the like. In more detail, such prior or historical userresponses are typically embodied in some form of data file, andretrieval, e.g. transmission, and analysis of such stored data may beemployed to generate and refine smart alerts functionality so as todetermine what smart alert led to a desired physiological response inthe past (and conversely what types of smart alerts led to anundesirable physiological response in the past). Such analysis mayinclude analysis of glucose trace data (and accompanying event data ifnecessary) to determine characteristics of desired and undesiredresponses, and then analysis of current physiological data to determinethe existence of a current diabetic state warranting attention. If asmart alert is determined to be appropriate for generation, the smartalerts functionality may cause selection of the type of smart alert thatled to a desirable physiological response in the past.

Detection and Use of Patterns

Patterns in glucose can be useful in understanding and helping patientsmanage their diabetes and how physicians manage their patients. Effortshave been made in the determination of patterns that highlight areasthat require or need attention by a user.

U.S. patent application Ser. No. 14/874,188, filed 2 Oct. 2015 (not yetpublished), entitled SYSTEM AND METHODS FOR DATA ANALYTICS ANDVISUALIZATION, and U.S. Patent Publication No. US-2013/0035575, filed 3Aug. 2012 and entitled SYSTEMS AND METHODS FOR DETECTING GLUCOSE LEVELDATA PATTERNS, both owned by the assignee of the present application andherein incorporated by reference in their entireties.

As noted above, pattern data may be employed in the determination ofuser cognition of diabetic states because if a user has a pattern of acertain physiological response, it can be inferred that upon therecurrence of such a physiological response, the user will recognize thepattern and take appropriate action. In other words, the user can beassumed to be cognizant of patterns experienced before, raising theestimation or prediction of user cognitive awareness. And further asnoted above, the recurrence of a pattern may be determined by storageand analysis of prior data, particularly those identified as patterns(but not necessarily patterns), and comparison of the same to acurrently measured (occurring) glucose trace to determine if thecurrently occurring glucose trace has curve or signature characteristicssimilar to those identified before.

One way of determining such patterns, or of detecting occurrences ofglucose events not in patterns, is by “binning” certain events definedby particular characteristics. That is, portions of glucose traces maybe detected that meet predefined criteria indicative of certain diabeticchallenges, e.g., rebound hypoglycemia, and then patterns may be lookedfor in these events that have been “binned” accordingly (or may bedetermined to not be in such binned patterns)

In more detail, and referring to the flowchart 300 of FIG. 6, asignature or “fingerprint” in glucose sensor data may be identified ordifferentiated within an individual patient based on some predefinedcriteria (step 36). Such criteria may include time-based criteria and/ormay include detected specific incidents within specific constraints. Inthe present system, according to present principles, bins may bedifferentiated by different criteria. A supervised learning algorithmmay be employed that allows for more bins to be learned for individualspecific patterns. For example, bins can be based on insulin data, rateof change of glucose data (or acceleration/deceleration thereof),patterns of data identified before and used to characterize data, e.g.,events occurring before small meals, responsiveness of an individual totheir glucose information, and so on.

A next step is that the incident may be characterized, e.g., based on adecay curve, waveform signature, or the like (step 38). Exemplaryincidents may include meal bolus indicators, e.g., based on insulindata, which may be classified into small, medium, and large meal bins.Such may correlate with insulin data. Different meal types may also becharacterized, which may then be sub-binned into different mealcompositions. These may correlate with rate ofchange/acceleration/deceleration. Incidents can also be characterizedbased on their correlation with events before a meal, and suchcorresponds to the patterns of data noted above. Incidents may furtherbe based on behavioral patterns, e.g., how often a user reviews theirglucose data or responds to alarms, and such can be correlated with theresponsiveness data noted above. It will be understood that other binsmay also be employed. Thus data about not only the incident but alsouser response data may be patterned, further providing data useful inalgorithms for estimating or predicting user cognitive awareness, whensuch incidents and/or user responses recur.

The characterized incidents may then be placed into a bin (step 40). Bythen synchronizing by incident, bins may be tuned for specific patientphysiology. The bins can then be normalized, i.e., a normal distributionof incidents in the bin may be defined (step 42).

The normalized bin information may then be used proactively as a datainput into the smart alerts functionality (step 44), e.g., in theestimation or prediction of user cognition. The binning technique may beemployed to determine when a patient is more tuned into their data,based on behavioral input, which may then allow for deductions,estimations, or predictions about user cognitive awareness.

Another use of such a binning technique is to identify good orsuccessful alert signatures (step 43), e.g., ones representing diabeticstates and alert types that the patient successfully responds to, frombad or unsuccessful alert signatures (step 45), i.e., ones representingdiabetic states and alert types that the patient ignores. Smart alertsmay then be provided based on this information, i.e., smart alerts maydetermine when and how to alert by comparison to the good alertsignatures (if the diabetic state is within a predetermined proximity(in glucose traces) to that in a good alert signature, then such mayprovide a trigger for a smart alert). In this case that the use of goodalert signatures does not depend on an estimation or prediction of usercognitive awareness, although the same may be used in combination withsuch estimations or predictions.

Patterns may also be identified by recognizing repeated occurrences ofconditions or events over the course of CGM wear. In order to find theserepeated occurrences, algorithms may synchronize CGM data, e.g., storedwithin buffers or data files, in each epoch (day, week, or month) andcompute an average or distribution of CGM values as a function of time.The synchronization may be done by absolute time in order to look for,e.g., nighttime lows or early morning or afternoon highs/lows. Oneproblem with this approach is that users may not always eat at the sametime or take insulin at the same time, and thus patterns may not beclearly apparent. Accordingly, in systems and methods according topresent principles, patterns may be identified that correspond to timeof food intake or insulin dosing. Such patterns may be identified usingthe techniques noted above. For example, a sudden change in glucosewithin a predefined duration in the morning can be identified as“breakfast”. With the use of smart phones and data communicationsbetween pumps and monitoring apps, data corresponding to a pre-mealbolus communication from a pump may be transmitted to a monitoringdevice such as a smart phone and used as an indicator for food intake.Other such indicators may include data from a GPS app, e.g., todetermine likely food-intake-related glucose changes as may occur atfrequented restaurants, changes in activity as determined byaccelerometer data for exercise related effects on CGM, and so on. Inthis way patterns may be (machine) learned about responses closest in“time proximity” to a meal, or to nighttime, or on “geographicproximity”, e.g., how far a user is from home or a source of food, andso on.

Once a specific time of synchronization is selected, data correspondingto CGM traces in each of these epochs can be overlaid on top of eachother to generate statistics. For example, an average of the traces canbe employed to distinguish real effects from random occurrences. Anytrue glucose effect due to a patient's physiology that is repeating islikely to be enhanced and other random effects are likely to be canceledor averaged out.

Data corresponding to distributions of CGM over time will provide themost likely glucose value after the synchronizing event and associatedminima and maxima. Accordingly, these can be employed to create typicalglucose variations in an individual due to the synchronizing event. Forexample, if glucose is synchronized based on food intake at lunch, thenthe post lunch glucose changes will capture typical changes in thatindividual. Any glucose changes beyond a predefined threshold may havean expected root cause. For example, an inadequate or missed bolus, aninsulin stack up effect, and so on.

Trends in glucose patterns (average, high, or low) may also indicateslow changes in behavior that may also be detected and alerted upon. Forexample, a slow trending of mean glucose or minimum or maximum glucoseas determined by machine learning may indicate a change in physiologicalparameters related to insulin dosing, e.g., insulin to carb ratio orinsulin sensitivity. Tuning these parameters with a machine learningalgorithm may also be performed using these patterns in cases wheredifferent parameters are needed for different times of day, week, ormonth. Where such trends are determined by the system to be occurringwithout a corresponding change in user behavior, e.g., as determined bymeal data, exercise data, data entered on a user interface, or the like,the algorithm may use such data to estimate or predict that the user iscognitively unaware of the same, and upon such an estimation orprediction a smart alert may be generated and displayed. For example, ifthe estimation or prediction indicates a likelihood of cognitiveawareness of greater than a threshold criterion level (and this isgenerally true for all estimations or predictions described herein).

These analyses can be run by algorithmic routines in the backgroundwhile the user is enjoying other functionality of a smart phone, suchroutines using individual patient data to learn from the data over timein a supervised fashion or an unsupervised one. Thus, over time, patternrecognition may be performed and smart alerts can enable and allow moreeffective alerting.

As noted above, implementation of smart alerts involves more than simplydisplacing or delaying alerts in time so as to make the same moreconvenient to a user. However, the measure of time as determined bytiming circuits or algorithms can be used along with additionalinformation such as behavior or context information in the prediction orestimation of user cognitive awareness as well as in the determinationof when and how to provide a smart alert. For example, the computingenvironment may identify an alert state, e.g., a diabetic statewarranting attention, but may time when to provide the smart alert basedon other input variables, including behavior and context data, i.e.,which are in many cases variables that go into the determination of thesmart alerts functionality. Even if, on this basis, a smart alert isdelayed, the determination of the delay will still be based at least inpart on real-time data as noted above.

Behavior and Context Inputs

Various types of behavioral and contextual information may also beemployed as inputs into smart alert functionality, to determine,predict, or estimate user cognitive awareness.

Contextual and behavioral information is data that generally correspondsto how a patient uses their mobile device/monitoring app, and thus givescontext to certain data determined by the device. Behavior inputinformation may be obtained via the system and can include an amount ofinteraction, glucose alerts/alarms states, sensor data, number of screenhits, alarm analysis, events (e.g., characteristics associated with theuser's response, time to response, glycemic control associated with theresponse, user feedback associated with the alarm, acknowledgment ofalerts or alarms, not acknowledging alerts/alarms within X minutes, timeto acknowledgment of alerts/alarms, time of alert state, and so on),diabetes management data (e.g., CGM data, insulin pump data insulinsensitivity, patterns, activity data, caloric data),data about fattyacids, heart rate during exercise, IgG-anti gliadin, stress levels(sweat/perspiration) from a skin patch sensor, free amino acids,troponin, ketones, adiponectin, perspiration, body temperature, userfeedback, and the like. The inputs may be provided by a sensor in datacommunication with the monitoring device. In some implementations, theinformation may be obtained through an intermediary such as a remotedata storage. User data noted above in connection with the userinteraction is an example of behavioral data.

Contextual information can include user location, such as determined bya GPS, WiFi, or the location of sharers and followers. The same mayrelate to a person's biology, location, sensing surroundings (e.g.,light, sound level), environmental data (e.g., weather, temperature,humidity, barometric pressure). The inputs may be received via apeer-to-peer or a mesh network via machine-to-machine communication.Context information can include daily routine information (which maychange especially from weekdays to weekends) from a calendaringapplication. Context information can include a frequency of touching orgrabbing the monitoring device, even if not interacted with, based on asensed motion of the device, e.g., from an in-device accelerometerand/or application.

Photos from a user's smart phone can be converted into contextual datausing image recognition algorithms. For example, photos of one or moreof: a glucose meter reading, an insulin pen or pump JOB, a location(e.g., a gym, park, house, Italian restaurant), or a meal may be used toprovide context information. The photos may be processed using imagerecognition algorithms to identify, for example, caloric intake for themeal shown in the photo. The type of insulin used, which may bedetermined by a barcode or the like imaged by a smart phone camera, mayalso be provided to the monitoring system as a useful input to theestimation or prediction of cognitive awareness. Indeed, reception ofsuch insulin type data itself may be indicative of user cognitiveawareness, particularly in combination with other data about the same.Context may also be provided by basal or bolus settings provided to ordetermined by the monitoring device. Such settings may be transmitted tothe monitoring device using known data transmission methods andprotocols, e.g., Bluetooth®. The transmission may occur on a push orpull basis, periodically, or on another basis.

Behavior/context data may be used in the system's prediction orestimation of user cognitive awareness as the same may indicate aknowledge of the user about their diabetic state. As one extreme,context GPS data may indicate the user is in their physician's office,and thus imply significant user cognitive awareness. At another extreme,behavior data may indicate sleep by way of accelerometer data, thusindicating significant cognitive unawareness. During such times, alertsand alarms may be appropriately modified, e.g., automatically enabled ordisabled. For example, if a sleep state is determined, thealerting/alarming system may enter a “night mode” or “sleeping mode”that is more conservative about glucose, and more aggressive with lowalarms. Such may then adjust system behavior, includingalerting/alarming, and may further adjust target ranges dynamically,significantly enhancing convenience to the user. For example, in such anight mode, the target range may adjust the alarm for hypoglycemia to bemore aggressive, e.g., somewhat higher, than in a “day mode”.Instigating or initiating these modes may programmatically transform themonitoring device such that its processing occurs in a different fashionthan before, enhancing efficiency of the computing device.

Other inputs to the estimation or prediction of cognitive awarenessalgorithm which constitute context/behavioral data may include certaindata types referenced elsewhere, such as exercise information from afitness bike or the like, glucose sensor information from a bloodglucose (BG) meter or CGM, insulin delivery amounts from insulindelivery devices, insulin on board calculations for the device, andother device-provided or calculated information. Othercontext/behavioral data inputs may include: hydration level, heart rate,target heart rate, internal temperature, outside temperature, outsidehumidity, analytes in the body, hydration inputs, power output(cycling), perspiration rate, cadence, and adrenaline level, stress,sickness/illness, metabolic/caloric burn rate, fat breakdown rate,current weight, BMI, desired weight, target calories per day (consumed),target calories per day (expanded), location, favorite foods, and levelof exertion.

For example, a high outside temperature coupled with low stress and highcaloric intake may be determined by the system to be consistent with theuser being on a vacation, which may in some individuals indicate alessened attention to diabetic state. In this case, the system maydetermine that a user is likely to be not cognitively aware of thediabetic state warranting attention, and thus that a smart alert shouldbe generated.

It is further noted in this regard that a high outside temperature maycause a smart alert to be rendered to the user regarding ensuring thattheir diabetes supplies are in a refrigerated container and are notexposed to high environmental temperatures.

For any of the above referenced behavior or contextual inputs, thesystem may be configured to receive and/or generate analytical metricsbased on the inputs. For example, a composite value may be generatedbased on the glucose level, temperature, and time of data generatedindex value for the user. The composite value may then be considered inthe estimation or prediction of cognitive awareness.

This information can be collected from various sensors within or outsideof the device, such as an accelerometer, GPS, camera data, and the like,as well as third-party tracking applications, including sleep cycleapplications, and may be used to affect outputs, as well. For example, aGPS may be employed to determine a rate of movement, so as to suppress asmart alert on a mobile device if in a moving car. In this context, thesmart alert may be transmitted to be rendered on a smart watch, however.Thus, real time measured sensor (glucose) data is used to determine adiabetic state warranting attention, and other data, which may be realtime or not (or a combination), is used to determine if a smart alertshould be generated. If a smart alert should be generated, then otherreal-time data, in the above example GPS data, may be used to furtherdetermine the form of the smart alert, and in particular the device towhich data is transmitted and rendered.

As noted, alerts can be affected by the proximity of sharers andfollowers. For example, when a sharer is in close proximity to afollower, alerts can become annoying as they may be activated in twolocations, e.g., on the sharer's pump, receiver or smart device and alsoon the follower's smart device. In one implementation, a follower appcan detect this situation and delay or suppress the alert on thefollower's device. For example, when the follower app receives an alert,it may start an RF, e.g., Bluetooth®, scan for the sharer's mobiledevice (or dedicated receiver or pump). If it detects the sharer'sdevice e.g., is within 30 feet (for a Bluetooth® detection), it canexamine the RSSI (received signal strength) to determine how close(e.g., very near, near, far) it is to the sharer's device. If thefollower device determines it is near or very near to the sharer, thefollower application can delay the alert for a minute or two to give thesharer a chance to respond. Alternatively, the follower app can suppressthe alert. In any case, if the sharer responds within a predeterminedtime frame, e.g., 1-2 minutes, then the alert may be suppressed on thefollower device. Beacon technology may also be employed for thispurpose, as disclosed in, e.g., U.S. Pat. No. 8,844,007, granted 23 Sep.2014 and entitled SYSTEMS AND METHODS FOR PROCESSING AND TRANSMITTINGSENSOR DATA; U.S. Patent Publication No. US-2013/0078912, filed 21 Sep.2012 and entitled SYSTEMS AND METHODS FOR PROCESSING AND TRANSMITTINGSENSOR DATA; U.S. Patent Publication No. US-2014/0273821, filed 14 Mar.2013 and entitled SYSTEMS AND METHODS FOR PROCESSING AND TRANSMITTINGSENSOR DATA; U.S. Patent Publication No. US-2015/0123810, filed 5 Nov.2014 and entitled SYSTEMS AND METHODS FOR A CONTINUOUS MONITORING OFANALYTE VALUES; U.S. patent application Ser. No. 15/001,756, filed 20Jan. 2016 and entitled CONTINUOUS GLUCOSE MONITOR COMMUNICATION WITHMULTIPLE DISPLAY DEVICES; and U.S. Patent Application No. 62/271,880,filed 28 Dec. 2015 and entitled INTELLIGENT WIRELESS COMMUNICATION FORCONTINUOUS ANLAYTE MONITORING, all of which are owned by the assignee ofthe present application and herein incorporated by reference in itsentirety.

In some cases, certain unexpected jumps in glucose as determined byanalysis of the glucose trace can temporarily disable sharerfunctionality, to avoid embarrassment to users, and to accomplish userprivacy goals and considerations. Such unexpected jumps can includecertain health or stress events. In one implementation, a thresholdlevel of an unexpected jump is predetermined, and the data of theunexpected jump is compared to the threshold level to determine if thesharer functionality should be employed to share data about the jump.

Heart rate data measured by a heart rate sensor may be employed in theestimation or prediction of user cognitive awareness. For example,off-the-shelf heart rate sensors may be used with measured resultscommunicated by an appropriate transmission protocol to the monitoringdevice or other devices providing smart alerts functionality. In anotherimplementation, the sensor electronics or transmitter (see FIGS. 45/46below) may be equipped with a strong light and an optical sensor (notshown) to detect heart rate. Such heart rate data may be used by itselfin the estimation or prediction of user cognitive awareness or it may beused indirectly to infer when a user is exercising, is undergoingstress, is asleep, and so on (which data may then be employed in theestimation or prediction). In a direct such use, a user may be in adiabetic state warranting attention. An accelerometer in a smart devicemay be used to determine that the device was just operated, and that theoperation included viewing of the glucose monitoring app user interface.If the heart rate is suddenly seemed to rise, it may be inferred thatthe user has been informed of the diabetic state warranting attention,and that no smart alert need be given.

Moreover, context and behavior may also be determined by use of socialnetworking information available about the user, where a socialnetworking feed, associated with the user, is arranged to provide asource of data to the smart alerts functionality. By analysis of suchdata in a social networking feed, user cognition data may in some casesbe determined, e.g., a user posting “I'm low right now.” or postingcontent similar to content posted when previously the user was low.Techniques such as natural language processing may be employed todetermine meanings of posts, and thus to allow a quantifiable measure ofsimilarity to prior posts. Thus, posts may be employed not onlydirectly, for their content (“I'm low right now.”), but also to inferthat the user is in a similar state as when the user previously postedsimilar comments.

Using such systems and methods according to present principles, theproblems encountered by prior monitoring devices, which lackedconsideration of such context/behavior aspects, may be effectivelyaddressed. In particular, where available data from sensors and othersources, including context/behavior aspects, allow an estimation orprediction of user cognitive awareness of a diabetic state warrantingattention, monitoring devices may be significantly improved by usingcognitive awareness as a way to suppress alerts when they are notneeded, which provides a significant technological advantage overmonitoring devices that base such alerts on thresholds only (or eventhresholds plus predictions). Such accordingly provide a significanttechnological advantage over prior monitoring devices, in which suchcognitive awareness was never taken into account. Additional detailsabout context and behavior information may be found in US PatentPublication No. US 2015/0119655, filed 28 Oct. 2014 and entitledADAPTIVE INTERFACE FOR CONTINUOUS MONITORING DEVICES, owned by theassignee of the present application and herein incorporated by referencein its entirety, and in particular at FIG. 4 and accompanying text.

Behavioral and contextual inputs may also be employed to provide otheruseful alerting functions. For example, if there are multiple CGMreceivers in a household, it becomes important for the users to be ableto uniquely identify each one. In order to do this, there are currentlyno visual aids other than a different colored case to help distinguishthe different units from each other. Thus, in systems and methodsaccording to present principles, as part of the setup procedure for anew receiver, or for one that is being used by a new user, the user canbe asked to select a unique identifiable mark, such as an initial,screen background, color theme, screensaver, animation, or the like, ora combination, that will be displayed in areas of the screen whendisplaying CGM information. For example, an initial may be displayed ina corner of the screen or in a status bar. The screensaver may beapplied when the screen is not displaying CGM information. The same maybe applied to entities such as fonts, backgrounds, and so on. Ananimation can be displayed in various selectable areas of the screen. Inthis way, a CGM receiver or CGM smart phone application can display anindicia of the appertaining user when displaying the user's CGM data,thus avoiding confusion when multiple data sources (e.g., hosts withsensors) are transmitting data in a common location at a substantiallycommon time.

Also as part of the set up procedure thresholds may also be set, theuser can indicate various types of alerts or alarms that they wish toreceive, e.g., a desire to receive alerts after versus before a meal.Smart alerts functionality or the smart alerts app may then beconfigured to query the user occasionally or periodically post set up,e.g., to determine if the user is satisfied with their set up selectionsor if they wish to revise them, e.g., to choose to receive alerts priorto meals. Such may also prompt and allow users to revise alertthresholds to better fit their lifestyle, i.e., perform alertoptimization.

Other Inputs

Other data sources besides user input, glucose data including deriveddata such as rates of change and pattern data, and sources ofbehavioral/contextual data, may also be employed.

For example, data derived concerning signal trends (besides patterns)may also be employed in smart alerts functionality. For example, smartalerts can be based on proximity and duration, e.g., at or near analertable zone, also termed “hovering”. That is, in many cases, even ifa user is not in a danger zone, if they are close to one for a longperiod of time, they want to be alerted. The smart alerts functionalitymay be employed to provide alerts in this situation. In other words, ina hovering situation, when a user is typically cognitively unaware oftheir proximity to a danger zone, a smart alert will be generated. Inthis case, data indicative of hovering may be determined by analysis ofmeasurement data, time duration data, and locations of thresholds. Ifthe user is close to a zone threshold, e.g., +/−10%, for a predeterminedduration of time, and if other data discussed above or below indicatesthat the user is not cognitively aware of the hovering situation, thenthe smart alerts functionality may cause the generation of a smartalert, where the smart alert indicates the existence of a hoveringsituation.

In another signal trend, a notification may be provided when a dangerousor undesired range is no longer an issue, thus potentially relievingstress on the user and notifying them that they are once again intarget. This is similar to home automation aspects of dimmer switches.In particular, when a dimmer switch is turned off, steps include: lightis on, then home automation sends a command to turn off the light, thenthe lights sends a command that it is starting to turn off the light,and finally the lights sends a command when the light is off. In thesame way, in a garage door situation: garage is open, then the homeautomation system sends a command to close the garage, the garage thensends a command that it is starting to close, finally the garage sends acommand when the garage is closed.

For the garage, this functionality is important because if something isblocking the sensor and not allowing the door to close, it is importantthat the user (through the home automation system) be aware that thegarage is not closed. In this case such notification can occur becausethe user never received the last command. In the present case, if apatient is in hypoglycemia or hyperglycemia, and tries to correct thesituation by taking a medical action, the sensor electronics may send anotification indicating that the glucose has inflected and is startingto rise (or fall). The same may then send a notification that the useris no longer in the hypoglycemic (or hyperglycemic range. Rather thanwaiting for the periodic (e.g., every 5 minutes) measurement todetermine if the patient is out of the undesired range, a more proactiveapproach may be taken to push a notification to announce that thepatient is no longer in a undesired range. Thus, in this case, a smartalert can be based on if the second notification is never received. Inparticular, where the system would properly expect an “out of danger”condition to be determined, where such determination never occurs, thesystem may infer that the user is still in a dangerous situation andthus that a smart alert should be generated. Such is particularly truewhere the user has taken a remedial action intended to treat thediabetic state warranting attention. In this latter case, the user maybe expecting to emerge from the dangerous situation without a furtherthought. Where this does not occur, a smart alert may be even moreimportant. It will be understood that this functionality may beaccomplished in numerous ways. For example, an “out of danger”notification may take the form of a flag that is set following thedetection of the inflection point. The location in time of the remedialaction, if known, may also be employed in the calculation,determination, and subsequent setting of an “out of danger” flag. If theuser does not emerge from the dangerous situation within a predeterminedtime duration (determinable from user history and patterns) followingthe remedial action, a smart alert again may be generated.

Another input can include a user life goal. In particular, diabetesmanagement goals, e.g., reduced hypoglycemic risk, reduced time out ofrange, reduced alert/alarms, postprandial optimization, reboundreduction, and so on, can be used as inputs to cognitive awarenessprediction and estimation. A user may set a goal, or even set differentgoals for different times of the day, and the system will alter orchange settings to enable the user to more easily achieve their desiredgoal. For example, a person might use a reduced hypoglycemic risk goalat night time, with the system using a predictive low alert with ahigher threshold setting. For these settings, e.g., where values areused during nighttime, a user is presumed to be generally cognitivelyunaware as they are likely sleeping. As another example, such a user mayalso have a postprandial optimization setting that reminds the user tobolus about 30 minutes before their typical lunchtime, or which providesa reminder to include protein with their meal in cases where anestimation or prediction is made that the user is cognitively unaware.

Other inputs include data and signals from insulin sensors, or fromother sources of data about insulin. For example, insulin sensor datacan be used to detect insulin delivery, which in turn provides a way ofestimating cognitive awareness of a diabetic state warranting attention,based on an estimation of when the insulin was injected. In more detail,if a hyperglycemic diabetic state warranting attention occurs, but thesmart alerts functionality app uses data from an insulin sensor todetect that there is a degree of insulin on board, then the smart alertsfunctionality may suppress an alert until such time as it is determinedthat the current insulin is no longer able to control the hyperglycemiaand that the user is not cognitively aware of a need for more. Insulinsensor data may be put to other uses as well. For example, besidesinsulin on board, information about timing of boluses may be employed tomodify the behavior of smart alerts for both hypoglycemia andhyperglycemia. For example, if a user has recently taken a bolus ofinsulin, threshold alerts could be delayed, or predictive alerts couldhave their target threshold temporarily suspended or elevated, based ona recognition of likely user cognitive awareness and/or lessened userdanger from the situation, e.g., lessening the danger of the “diabeticstate warranting attention”. As a particular example, if a predictionwas used at 200 mg/dL, then knowledge of a bolus could set the alert to250 mg/dL for one hour. Similarly, for a low glucose alert, knowledge ofinsulin could increase or decrease the sensitivity of the alert if thecalculation suggests that the amount of insulin suggests a more modestor aggressive glucose drop.

Generally use of insulin sensor data in this context requires a degreeof machine learning, particularly as each user has a different insulinsensitivity, and this sensitivity may change over time. Thus, knowledgeof current insulin sensitivity can be a prerequisite for use of insulinsensor data, particularly when a high degree of accuracy is needed.

As another example, if information or data is known about insulin onboard, or is subsequently or contemporaneously entered by the patient,the same may be used in the determination of when to provide smartalerts. Such information about insulin on board may be entered by thepatient or received from a medicament delivery device, e.g., pump orpen. In one implementation, the user may input how much insulin theyhave taken, and then a calculation may be made as to how much insulin isremaining in their system over the next several hours. The insulin onboard value may then be employed to notify when a patient is notified,e.g., is alerted or alarmed. In one implementation, if the patientnormally wants to be alerted when they go above 200 mg/dL, and thesystem detects that they are above that value, or predicts that theywill go above that value, the system may then determine or receive dataabout the insulin on board. If they have a lot of insulin in theirsystem, e.g., five units, then the system may determine that the patientneed not be alerted immediately, because the insulin would be takingcare of the potential hyperglycemic condition. Similar steps may betaken as the patient approaches hypoglycemia. For example, if thepatient desires to be notified if they drop below 80 mg/dL, and thesystem detects that they are above this value, but have a significantlevel of insulin in their system, then the system, i.e., with smartalerts functionality, may be caused to alert the user earlier that theyare heading low.

Another potential input includes type of diabetes and the particularmanifestation of diabetes for a given user. The cognitive awareness of atype I patient may be different from the cognitive awareness of a typeII patient. Thus, the generation of a smart alert may differ between thetwo, and the timing of the alert may similarly differ. In oneimplementation, the difference between the two situations is limited tothe threshold level at which the estimation or prediction determinescognitive awareness. In other words, the threshold level is alteredbetween the two types of diabetes, the threshold level being that whichis compared to the estimated or predicted cognitive awareness, and whichresults leads to the generation (or not) of a smart alert.

Other different and individualized physiology and pattern effects may beseen. For example, it may be common for a patient to hover around 70mg/dL, but it may be very uncommon for that same patient to suddenlydecelerate after they pass through 60 mg/dL. In this example, bydetermining the glucose concentration value and including in theestimation or prediction the rate of change of the same, an atypicalresponse may be detected and alerted upon.

Systems and methods according to present principles, incorporatingcognitive awareness in the determination of whether to alert users, arecustomized and/or two and for each individual user, and thecustomization/tuning occurs by machine learning, e.g., using data andsources noted above. Other significant sources of customization orpersonalization include varying the operation of the smart alertsfunctionality based on physiology, age of the patient, exact diagnosis,and so on. Thus, implementations of systems and methods according topresent principles provide a significant advantage in the reduction ofburden on the user or clinician, e.g., of setting alarm and alertthresholds, which in some cases are not even knowable because ofday-to-day variations. That is, in many cases, there is simply no wayfor a user to figure out how to customize their alerts without thetechnological advancement of present systems and methods and theirsubsequent prediction or estimation of cognitive awareness.

In other variations, systems and methods may create multiple profilesfor a patient, depending on activities, illness, pregnancy,menstruation, other cycles, and so on.

Yet other data sources may include telemetry, metabolic rate, and so on.Still other potential data and data sources may include correlation data(such as user cognition at night versus during the day), pain data,heart rate variability, stroke volume, cardiovascular health, ability todistribute insulin, body temperature (which affects insulin absorptionrate), insulin type (based on insulin sensitivity measurements,profiles, peaks, time between peaks), atmospheric pressure (whichaffects CGM and insulin absorption), insulin sensitivity, determinationsas to which factors bear most heavily for a given user, e.g., exerciseversus meals, health or physiological conditions known to affect certainparameters, illness, whether a smart device is in a particular mode suchas airplane mode, exception management (e.g. to identify what is normalfor a particular patient and to run exception management rules), whetherthe smart device running the smart alerts functionality is in a trainingmode, clinician set up parameters, response triggered data, and so on.Thus, user cognition may relate not just to whether a user is aware of ahigh or low, but also whether the user is encountering a situation witha highly complex response that the user simply cannot be cognitivelyaware of due to its inherent complexity.

For any given input, fuzzy logic may be employed in the determination ofthe value of the input. In some cases, fuzzy processing may also beemployed, as well as fuzzy outputs. In more detail, alerts or alarms,including smart alerts and alarms, can be triggered in a way that doesnot rely solely on the glucose value crossing a numeric threshold. Inone implementation, an algorithm may be employed that triggers an alertwhen a user's glucose value is hovering just below a high alertthreshold or just above a low alert threshold for a given predeterminedduration, even if the threshold is not crossed. This implementation issimilar to the hovering implementation described above. For example, auser's high alert threshold may be set at 180 mg/dL, and the user'sconsecutive glucose values may be 178 mg/dL, 175 mg/dL, 177 mg/dL, and178 mg/dL. While the user's glucose value never reaches 180 mg/dL, thealgorithm recognizes the proximity of the glucose value to the thresholdand provides a smart alert to the user after 20 minutes (or after someother duration which may be configurable by the user). Suchimplementations are useful because the user may be prompted to takecorrective action, e.g., a small insulin bolus or exercise, when theuser would otherwise not have paid attention to their glucose value. Inanother implementation, an algorithm may be employed that attenuates thealert/alarm when the user's glucose value is crossing back and forthover the alert threshold but with a small rate of change. As an example,a user's high alert threshold may be set at 180 mg/dL, and the user'sconsecutive glucose values may be 170 mg/dL, 178 mg/dL, 182 mg/dL, 179mg/dL, 181 mg/dL, 178 mg/dL, and 182 mg/dL. An alarm would be triggeredat the transition from 178 mg/dL to 182 mg/dL, but if the useracknowledged or dismissed that alarm, the alarm would not be triggeredagain at the subsequent transition from 179 mg/dL to 181 mg/dL. Such animplementation may be particularly useful because it helps avoid theannoying situation users face when they are aware of the borderlineglucose value and do not want or desire or need repeated alerts. In someimplementations, use of this technique may be more safely performed atthe high alert threshold then at the low alert threshold.

Other variations may include variations in a frequency in which inputdata is received or output data is displayed. In particular, systems andmethods according to present principles may be configured to receiveadditional data, e.g., by updating more often, when a dynamic risk is onthe horizon, e.g., updating every minute instead of every 5 minutes. Forexample, if an impending low is predicted based on a current glucoselevel and glucose rate of change, then the display may update everyminute instead of every 5 minutes. In such an implementation, moreadvanced information may also be displayed, such as an indication as towhether a rising or falling glucose trend is accelerating. More advancedvisuals may also be employed to indicate this deduced, calculated,estimated, or predicted information. In this way, the user is providedwith better and more frequent information when they need it most. And byuse of the most accurate information, the system may be enabled to evenfurther suppress alerts, e.g., in cases where a dangerous situation isfixing itself. In this way, user annoyance at unnecessary alerts isfurther avoided.

As another example of an input to the estimation or prediction ofcognitive awareness, signal metadata may be employed. For example,inflection points may be used which, once determined, cause a focusingon the area of inflection. As a particular example, the system cansample more often at inflection points than at non-inflection points.Such inflection points may include points at which a glucose signal isturning around or other such points where fine tuning or additional datamay be useful in determining parameters helpful to a user, includingparameters determinative or useful in determining user cognition ofdiabetic states warranting attention. The benefits are as noted above.It is noted in this regard that sampling more at such points allowssampling less at different points, and sampling less at different pointsmay be particularly useful in saving battery life, reducing powerrequirements, sensor and monitor life, and so on. More frequent samplingand subsequent transmission of data for rendering may also be employedin situations where the user is nearing or is in hypoglycemia orhyperglycemia. In other words, such as zones may be used as theinflection points noted above.

Inputs may be received in some implementations from wearable sensors. Inone implementation data available from a smart watch may be used eitheron a standalone basis or to augment other data. In a particular example,sensors and signals collected by smart watches such as the Apple watchand the Microsoft Band may be employed to augment detection ofhypoglycemia. Such signals can include those from heart rate sensors,sympathetic/parasympathetic balance (which can be inferred from heartrate), perspiration/emotion/stress from conductance sensors, and motiondata from accelerometers. Such signals may be used in addition to theCGM signal. The algorithms used to process these auxiliary signals canbe trained on the patient's own data, using CGM to assist in thetraining. These algorithms can be optimized off-line, e.g., in thecloud. Then detection criteria can be sent to the patient's smart phoneand/or smart watch. There may be instances when CGM fails to detecthypoglycemia, but when augmented with auxiliary signals indicatingpossible hypoglycemia, the patient may be alerted to the suspectedhypoglycemia and thereby enabled to avoid the consequences.Alternatively, after the algorithms used to process the auxiliarysignals have been trained, the smart watch signals may be able to detecthypoglycemia without the use of CGM. In this use case, adjustments tothe algorithms may be necessary to optimize sensitivity or specificity.

In other implementations, smart watch sensors and machine learning maybe employed to detect and quantify sleep. For example, if a user isasleep, as determined by motion data from an accelerometer, it may beinferred that the user is not cognitively aware of a diabetic statewarranting attention, or indeed any diabetic state. Thus, alarmvariations may be configured to not suppress any smart alerts if thesystem estimates or predicts that the user is sleeping. Other sleepsensors may also be employed in the estimation or prediction ofcognitive awareness.

As an example of an alternate type of sleep sensor, temperature may beemployed as a mechanism to determine whether or not a patient issleeping. For example, such sleep sensors may be employed to observetimes awake versus times spent sleeping. In implementations, atemperature sensor mounted on the skin, which may be a separatetemperature sensor or one implemented within the adhesive patch attachedto the patient, may take advantage of a strong correlation betweentemperature data and time spent asleep.

Still other inputs will be understood as disclosed in U.S. PatentApplication Ser. No. 62/289,825, filed 1 Feb. 2016, and entitled SYSTEMAND METHOD FOR DECISION SUPPORT USING LIFESTYLE FACTORS, owned by theassignee of the present application and herein incorporated by referencein its entirety.

Outputs

The output of the smart alerts functionality is generally a displayedoutput, but the algorithm itself may have an “output” in the sense of acalculation, estimation, or prediction of user cognition, and thus thecausing the suppression of an alert or simply causing the generation ofan alert step to not occur (if, e.g., the user is estimated or predictedto be cognitively aware of the diabetic state warranting attention) arealso considered “outputs” in this sense.

The output of the smart alerts functionality may also be in a number offorms, e.g., a visual display, an audible indication, a tactileindication, and so on. Such may be combined in various ways to supportsmart alerts functionality. For example, a visual display may beemployed to provide an indication of the diabetic state, but an audibleor tactile indication may be provided according to smart alertsfunctionality based on cognitive awareness. Alternatively, a visualdisplay may be employed to provide an indication of the diabetic state(e.g., a value and a trace graph), but another visual display, e.g.,overlaid on the first, may be employed to provide a smart alert. Inanother implementation, a prompt on the display may be employed and usedto test for user cognitive awareness, and if such indicates the user iscognitively unaware, then a smart alert may be generated. How the promptand its response indicates cognitive awareness may vary. The prompt onthe display may be explicit, asking the user if they are aware of animpending diabetic state warranting attention, or may be more subtle orimplicit, requesting lesser information, but which would provide datanecessary for the estimation or prediction of cognitive awareness. Suchimplicit or subtle prompts or questions may be more appropriate foryounger users or less experienced or sophisticated users, or those whohave less experience with the biological symptoms of diabetic states.Thus, systems and methods according to present principles may use userprofile information in the determination or calculation of what types ofprompts or questions to render on a user interface.

The resulting rendered user interface in the output of the smart alertsfunctionality is strongly tied to the calculated estimation orprediction of user cognitive awareness. If the user is predicted orestimated to be cognitively aware of the diabetic state warrantingattention, then the user interface will generally not display a smartalert. Conversely, if the user is estimated or predicted to be notcognitively aware, then the smart alert will be displayed, and the samewill generally involve an alteration in the user interface. Such anoutput is generally believed to be far more effective and efficient forusers then alerting based only on thresholds, for the reasons givenabove, e.g., as smart alerts cause less re-alerts, less alert fatigue,and so on. The same further allows significant savings of battery powerand computing cycles.

In a given rendered output, the smart alert may further be displayedalong with an expression of confidence or doubt. The level of confidenceor doubt may be calculated by systems and methods according to presentprinciples based on error bars calculated for data, known or determinedcalibration ranges or errors, known or determined sensor errors, or thelike. The confidence or doubt may be expressed to foster trust betweenthe user and the system. This trust is heightened when the system hasperformed additional machine learning steps, and has obtained enoughdata to make accurate, highly personalized suggestions andrecommendations for a user. At such a time, systems and methodsaccording to present principles may reduce the display of expressions ofdoubt in the smart alerts output, as the same are no longer or lesspertinent. As a particular example, a low alert that occurs during apossible artifact (e.g., a “dip and recover” fault) may be expressedwith a degree of doubt. An insulin dose recommendation may be made toaccount for the uncertainty in the glucose estimate. Such may occurduring, e.g., day one of a CGM session. The recommendation expressesthis uncertainty to the user in a way that in turn fosters trust in themonitoring app and smart alerts functionality, because the user is awarethat the system is making a “guess” or prediction rather than making anunequivocal recommendation. In this way, the smart alert engages theuser and fosters trust in the system. Later, after the fault is nolonger present, the monitoring app and smart alerts functionality mayexpress data without a degree of doubt, or with a lessened degree ofdoubt, heightening confidence in the monitoring app, not just in itsaccuracy but also in its error awareness and handling.

Where a smart alert is generated and displayed, the same may beconfigured on the user interface to be minimally intrusive to the user,and may be such that the rendering of the glucose value itself isdeemphasized while trend information, e.g., achieved by rendering on thedisplay or screen various arrows or zones, is emphasized. Such may beimplemented by a control setting, adjusted by a physician or by theuser, that adjusts the rendering of the display to focus on trend ortrend arrows, with a less visible glucose number.

Aggressiveness of the smart alerts functionality can be tuned byuser-configurable settings, e.g., slider bars. The slider bars canaffect not only the inputs but also the outputs. That is, slider barsmay be employed to affect the operation of the smart alertsfunctionality on the input side and also on the output side. For a userwho desires settings with high aggressiveness, more smart alerts willgenerally be provided than for users who desire a lesser degree ofaggressiveness. Put another way, if the user interface setting is set ata high degree of aggressiveness, the system may automatically control athreshold level of cognitive awareness (on which smart alerts are based)to be higher. If it is higher, more smart alerts will be generated, asthe threshold level for cognitive awareness is more difficult to attain.In this way, the system becomes more aggressive. Conversely, if the usercontrols the user interface setting to be a lower level ofaggressiveness, the threshold level of cognitive awareness may bedecreased, causing fewer smart alerts.

Dynamic risk may play a role in how rapidly input data is accumulated.Dynamic risk may also play a role in how often data is updated,providing a more granular and accurate way for a user to receivenotification of and assessment of their risk. This risk may be measuredby a suitable calculation based on glucose value and rate of change, ormay include more advanced calculations such as including glycemicurgency index as described in the patent application incorporated byreference above. That is, based on a dynamic risk calculation, which caninclude glucose value, glucose rate of change, and other more complexderived values (generally involving one or more of these real-timevalues), the system can automatically adjust data transmission settings,e.g., frequency, whether pushed or pulled data is used, screen refreshrate, data updating and recalculation rates, calibration frequency, andso on.

Where smart alerts are generated and rendered on a display and moreparticularly on a user interface rendered on the display, they may takea number of forms including having various levels of prediction. Forexample, referring to FIG. 7, a smart alert may indicate a diabeticstate warranting attention and may further provide details of currentglucose values, expected glucose values, e.g., expected within a certaintimeframe, e.g., 20 minutes, and so on. The smart alert, shown in FIG.7, may be overlaid on top of a trace graph, the trace graph indicated inFIG. 8. As may be seen, the smart alert of FIG. 7 provides a detailedalert to a user who is not cognizant of their diabetic state warrantingattention, in this case of an impending low (shown by the local minimaof FIG. 8).

FIGS. 9-29 illustrate exemplary user interfaces and smart alerts, whichhave been constructed based on considerations of interface usability andother factors. For example, while the use of displayed arrows issometimes helpful to users, their meaning is on occasion not clear. Forexample, arrows tend to convey a sense of urgency, or a need to takeaction, but users may be confused about whether the arrow is referringto a prediction or a trend. Thus, in one implementation, it has beenfound useful to display on the user interface a current value ofglucose, a threshold alert level, e.g., the most relevant thresholdalert or alarm level given the current value of glucose, e.g., ‘55’, asymbol, and a color, e.g., yellow or red in the case of a diabetic statewarranting attention for which the user is cognitively unaware, theparticular color depending on the urgency of the state.

The symbol may be as indicated in FIGS. 9-14, e.g., a dotted linestarting at a bolded point and terminating at a threshold value (FIG.9), a dotted line with an arrowhead indicating direction (FIG. 10), adotted line which, if read from left to right, indicates a direction(FIG. 11), a line segment with an arrowhead indicating directionality(FIG. 12). In some cases, e.g., for sophisticated users, just a warningis necessary, and such is shown in FIG. 13. A warning with a trend arrowand color indicator are shown as an alternative user interface in FIG.14. All of these elements, and their combination, are useful inproviding context to the smart alert. Symbols may be particularlyconvenient for use with children, as the same are more readilyidentifiable and easy to communicate to parents.

As can be seen in FIGS. 9-14, the smart alerts are generally provided asan overlay above another user interface, which is typically associatedwith a CGM monitoring application. Thus, the overlay is often above atrend graph, which may or may not include a predictive element,sometimes indicated by an arrow or dotted line. In general, it has beenfound that the appearance of two different arrows on the screen may beconfusing to users, and thus if a trend arrow appears in a smart alert,the same should not appear or should be suppressed in an underlyingglucose trace chart.

It has also been found beneficial to indicate an endpoint of the trend,and such is indicated in FIGS. 15-17. This endpoint may indicate a time(quantitative, such as “within 20 minutes”, or qualitative, such as“soon”), a value, or both. In FIGS. 15-17, the time portion of theendpoint is indicated by a segment on a clock indicating 20 minutes, aswell as qualitatively or quantitatively within the textual warningitself. The value portion of the endpoint is indicated by, in this case,a low alert threshold, e.g., 55 mg/dL. A current value is also shown inFIGS. 15-17, along with a trend arrow and a color indicator. The colorindicator is generally based on the urgency of the smart alert, asdetermined by the current glucose value, its rate of change, and thelike.

FIGS. 18 and 19 indicate a glucose trace chart (FIG. 19) and a smartalert overlaid on the same (FIG. 18) and similarly FIGS. 20 and 21indicate a glucose trace chart (FIG. 21) and a smart alert overlaid onthe same (FIG. 20). These figures show a smart alert that has been foundbeneficial, and the same includes a textual warning “urgent low soon”, acurrent value of glucose, i.e., 93 mg/dL, a trend arrow, an indicationof the relevant threshold, i.e., 55, and a time indication indicated bya clock segment. FIGS. 20 and 21 further include an indication of thetime endpoint within the textual portion of the smart alert warning.

In many cases, as illustrated by FIGS. 22-29, it has been foundadvantageous to display quantitative time-based warnings rather thanqualitative ones. FIGS. 22-29 illustrate a time sequence of smartalerts, showing a time progression over a 15 minute time span. FIGS. 23,25, 27, and 29 show underlying glucose trace graphs, and theirrespective smart alerts are shown by FIGS. 22, 24, 26, and 28. Aninitial time point is indicated by FIGS. 22-23, with a time point 5minutes later shown by FIGS. 24/25. A time point 10 minutes later isshown by FIGS. 26/27, and a time point 15 minutes following the first isshown by FIGS. 28/29.

As noted above, the use of the color red has been found beneficial inthe provision of smart alert. However, the same may be modified based onthe urgency of the smart alert. For less urgent smart alerts, forexample, a lighter shade of red may be employed.

In another implementation, a smart alert may be provided on the lockscreen of a smart phone. FIGS. 30-41 indicate such an implementation,both generally and within the context of a low glucose alert. As may beseen, when a user sees a notification on their smart phone (FIGS. 30,34, and 38), they may unlock their phone and see a “pop over”notification of the alert (FIGS. 31, 35, and 39). An “okay” button isshown, and upon activation, a trend screen may be displayed (FIGS. 32,36, and 40). In these figures, a banner covers other navigation buttonswithin the application, the inability to access such navigationfunctions reaffirming the importance of the alert. The user can closethe banner alert by tapping the X button and continue using theapplication. Alternatively, the banner will disappear once the usercorrects their glucose to raise their glucose value to a satisfactorylevel. In alternative implementations, the alert banner may be relocatedbelow the navigation pane, enabling the user to use the navigationfunctions even when the alert is displayed. Such may be configured to bebased on the urgency of the alert.

In the displayed user interface implementations, or in any otherimplementations, tapping an appropriate icon displayed on the screen,e.g., a magnifying glass, may cause the display of additionalinformation or could invoke other functionality. For example, tapping anappropriate icon could lead to various levels of prediction beingdisplayed, e.g., indicating to the user what is expected over the next10 or 15 minutes. Such prediction may be provided by textual indicators,a dotted line on a glucose trace graph, arrows, time- andglucose-indicating endpoints, and the like.

In another embodiment, smart alert functionality may be implemented suchthat an alert or alarm sound volume automatically adjusts to beappropriate given a detected ambient noise level. In other words, givena detected ambient noise level, the volume of the alert or alarm may beadjusted such that a signal-to-noise ratio is sufficient to allow a userto hear the alert or alarm. Such volume may also be user customizable.The detection of the ambient noise level may be implemented by includinga microphone or other sensor to detect and measure the same. In a smartphone implementation, the phone microphone may be used. Put another way,the system measures or detects an ambient noise level, and automaticallyadjusts an alert or alarm sound volume to achieve a desiredpredetermined signal-to-noise ratio or alternatively SINR.

A default volume setting may be provided that is sufficiently greaterthan an assumed ambient noise level. When the measured noise level islouder than that assumed by the default, or the noise level detected orassumed during customization, the alert/alarm volume may beautomatically increased, either on a gradual basis or suddenly, tolevels such that the user can hear the alert or alarm. For example, thedecibel level of the alert/alarm may be set to have a predeterminedrelationship with the ambient noise level, e.g., to achieve thepredetermined signal-to-noise ratio noted above. The noise level in suchimplementations can be advantageously measured just prior to theissuance of an alert/alarm.

In other implementations of the outputs, where the user has experiencedan unpleasant event, e.g., hypoglycemic, hyperglycemic, out-of-controlglucose, or the like, systems and methods according to presentprinciples may automatically generate a post-event smart alertindicating some degree of forensics, informing the user as to how theevent occurred, and providing helpful tips to prevent future suchevents. Such automatic generation of forensic information may beparticularly provided where the system, by virtue of determination ordetection of a signal characteristic or other diagnostic information, isable to uniquely identify the cause of the unpleasant event. Byproviding such forensics, the user becomes informed that the smartalerts functionality is configured to track, detect, and is programmedto alert on such unpleasant events, increasing user confidence and trustin the system. In addition, data about such events may be advantageouslyemployed to refine the operation of the smart alertsapplication/functionality through the use of machine learningalgorithms.

The system may further be configured to indicate a caution or othermodifier associated with a smart alert. For example, the system couldprovide an indication such as “I'm waiting an hour before I alarmbecause I'm waiting for [a known “on-board” therapeutic aspect, e.g.,insulin or food or exercise] to kick in. But be aware that there may bean alert or alarm about this condition on the horizon.” Such anindication provides a middle ground where the system may have someconfidence in an estimation or prediction of user cognitive awareness,but the confidence is not unequivocal; equivalently, the level ofestimation or prediction is not enough to unambiguously establish usercognitive awareness or user cognitive unawareness. In a particularimplementation, such cautions or modifiers may be configured to appearalong with the smart alert (via various string manipulation subroutines)whenever the estimation or prediction of cognitive awareness is within5% of the threshold level. It will be understood that the content of thestring modification will vary depending on the type and content of thesmart alert.

Certain smart alerts functionality may be leveraged even during a sensorwarm-up period. In more detail, when a new sensor is installed, a periodof time generally exists when no information is available, e.g., twohours. During this time, the blood glucose values are not ensured to beaccurate. However, trend values may still be obtained, and the displayof these advantageous for a user. A finger stick blood glucose value maybe employed during this warm-up period to obtain an accurate glucosevalue, and in some cases system analysis of the indication of the trendobtained during the warm-up period may be employed as a trigger toobtain such a finger stick value. In an implementation, if the glucosemonitoring algorithm used the last known reading from the removedsensor, the algorithm may automatically cause an instruction or a userprompt indicating that the user should perform a finger stick as asafety caution. For example, if the last known glucose value from theremoved sensor was 76 and had a downward trend, then reduced ion countsduring warm-up may indicate that the user is trending towards ahypoglycemic event. As the actual glucose value data is unavailableduring warm-up, the estimation or prediction of user cognitive awarenessof the diabetic state warranting attention will be correspondinglylower. In this situation, a smart alert may be generated that the usershould perform a finger stick. In another example, if the last knownglucose value from the removed sensor was 76 and was relatively stable,but real-time reduced ion counts are being measured during warm-up, thesame may again indicate that the user is not cognitively aware of apotential hypoglycemic situation, particularly as the user did not evenhave the benefit of an indication of a downward trend during the priorsensor session. In summary, the smart alerts functionality may employprior sensor session data as well as real time trend information in thegeneration of smart alerts, where the generation is based on a level ofuser cognitive awareness, generally compared to a threshold criterion,which can itself depend on one or more data inputs.

In a further implementation, it is noted that CGM and low glucose alertthresholds assist users with identifying impending hypoglycemia so thatthe users can take action to avoid or minimize the hypoglycemic episode.Such features are particularly useful when patients have difficultyidentifying low glucose based on physiological symptoms such asshakiness, sweating, and so on, and these correspond to users withimpaired hypoglycemia awareness. Hypoglycemia awareness is variable fromepisode to episode, and has been shown to be impacted by recenthypoglycemic events. Notably, recent hypoglycemic events reduce a user'sawareness of subsequent episodes.

Accordingly, in this further implementation of systems and methodsaccording to present principles, an alert characteristic may be modifiedas a function of a recent history of hypoglycemic episodes, such thatalerts are more likely to be provided earlier or be more salient insituations where the patient is least likely to become aware of theirhypoglycemia as a result of their symptoms. Typical alertcharacteristics to be modified include, e.g., threshold, intensity,visual display, and so on. Such an implementation minimizes the numberand/or annoyance of nuisance alerts (alerts provided when the patient isalready aware of the hypoglycemia, or provided more frequently thandesired) when alerts are less valuable and maximizes the sensitivityand/or saliency of alerts when patients cannot rely as much on symptoms.Put another way, user cognitive awareness may further be based on recenthistory of hypoglycemic episodes, as such have been shown to reduce userawareness of subsequent episodes. In an implementation, recent historyof hypoglycemic episodes may be gleaned from past or historic data, andused in combination with real-time data, e.g., glucose data, glucoserate of change, and so on, particularly real-time data indicatingpotential hypoglycemia, to result in a data output useful in theestimation or prediction of user cognitive awareness. The data outputuseful in the estimation or prediction of user cognitive awareness maybe used, e.g., to raise or lower the threshold at which a user isestimated or predicted to be cognitively aware. Where there is a recenthistory of hypoglycemic episodes, the threshold may be raised, thusleading to additional smart alerts. Where user data indicates that afrequency of hypoglycemic episodes is reduced, e.g., due to betterdiabetes management by the user, the threshold may conversely be raised,reducing the number of smart alerts.

In alternative implementations, user interface-displayed smart alertsmay incorporate or have outputs that may take any form noted in USPatent Publication No. US-2015/0289821, entitled GLYCEMIC URGENCYASSESSMENT AND ALERTS INTERFACE and owned by the assignee of the presentapplication and herein incorporated by reference in its entirety.

Use with Pumps and Other Delivery Devices

Smart alerts may be beneficially employed in combination with data frominfusion pumps and other such delivery devices. In part the use of smartalerts will be dictated by the use of the delivery device, whether in anopen loop system, a closed loop system, or a semi-closed loop system.

In an open loop system, the smart alerts may be used as described above,providing alerts on, e.g., bolus information, where the user isestimated or predicted to be cognitively unaware of a diabetic statewarranting attention. For example, in the case of a meal bolus, machinelearning may be employed to determine a meal pattern of a user, andwhere a glucose trace is encountered after a meal that is not part of atypical meal pattern, a smart alert can be generated based on such datato suggest a bolus. Besides determining that a bolus should be infused,the machine learning and smart alert may further be employed todetermine the timing, e.g., preferably not too late or too early. Suchlearning typically involves analysis of past data to learn at whichpoint in time during a “meal episode” the bolus should be delivered. Inthis case, past data, meal episode data, is used in combination withreal-time data, e.g., indicating that the user is about to eat a meal oris approaching a typical meal time (determinable by clock data or eventdata or the like). Similarly, machine learning may be employed todetermine that the user often overboluses, e.g., or causes so-called“rebound” or “rage” boluses. Such may be particularly relevant where thesmart alerts app or functionality receives data from a pump or pen. Ifthe system can determine that the user has a trend or pattern towardsuch overbolusing, the trend or pattern information may be employed toprovide a smart alert to caution the user against the same. Moregenerally, the information may be used to inform smart alertsfunctionality generally. Such may be particularly important during timesin which a user is trending upward, e.g., towards hyperglycemia, as suchtimes are particularly susceptible to overbolusing.

As another example of the use of smart alerts in the context of deliverydevices, when a user changes an infusion set, there is a higher thanusual risk of getting an infusion site or cannula that does not haveproper insulin absorption or which is occluded. While occlusion alarmsexist, the same are generally not useful at detecting problems with theinfusion site, particularly if the cannula is not fully occluded. Thestandard of care dictates that users check their blood glucose levelstwo hours after changing their infusion sets to ensure that they aregetting proper insulin delivery. Undetected cannula problems can lead tohyperglycemia and even ketoacidosis, which can be life-threatening, andconstitutes the main risk of using pump therapy versus multiple dailyinjections.

Systems and methods according to present principles can detect problemswith the infusion set early on, before the patient develops ketones, andcan employ smart alerts to provide an alert about such a diabetic statewarranting attention to a user, who in such situations is generallycompletely cognitively unaware of the existence of the problem. Forexample, knowing when a patient changed their infusion set can be usedin the determination of a probability of a bad infusion site, and suchdata can further be employed to distinguish between other rises in bloodsugar and infusion set problems. In particular, the system can employdata about cannula fills, reservoir changes, and pump primes todetermine instances when a user has changed all or a portion of theirinfusion set, and such data can be used in combination with CGM data aspart of the estimation or prediction of user cognitive awareness; fluidpressure data is useful in learning individualized alarms for possibleinfusion set problems, and can be similarly used. While CGM data andpump data used separately are generally insufficient at detectinginfusion set problems, together they provide more specific alarms thatcan prevent dangerous events, particularly hyperglycemic events. Such inparticular can be used to determine and alert on infusion set problems,alerting the user to address the problem before the onset of medicalissues, including ketoacidosis.

In particular, and referring to the system 350 of FIG. 42, the system ofFIG. 1 is illustrated as also receiving data from a pump 52 (or a sourceof pump data). By analysis of pump data, e.g., by comparing receivedpump data to patterns or signatures of known pump data, particularlyassociated with issues preventing proper operation of the pump, the CGMapp can detect cannula/infusion set problems and provide smart alertsthereabout. Such problems are particularly and notoriously difficult forusers to detect, and thus may weigh heavily in prediction or estimationof user cognitive awareness of the diabetic state warranting attention.Thus, and referring to the flowchart 400 of FIG. 43 (based on theflowchart 101 of FIG. 2), the method of providing smart alerts mayinclude a step of receiving pump data (step 54), and the same may beemployed in the calculation of the estimation or prediction of usercognitive awareness.

Pump data received in step 54 may include data relating to the bolusinformation, pump shutoff data, pump alarms, pump rewind (time), pumpprime (time), cannula fill (timing and amount), fluid pressure or otherdata employed to generate occlusion alerts, and so on. In oneimplementation, APIs allowing access to such data may be employed inmonitoring applications. Conversely, smart alerts functionality may alsobe provided within the context of an application operating andcontrolling a delivery device, and in this case data from a CGM app orother monitoring app may be employed via an appropriate API to the smartalerts functionality running on the delivery device.

Thus, where users are not cognitively aware of pump problems such asocclusions, smart alerts may be provided to inform them of the diabeticstate warranting attention, and thus provide an alert that is moreeffective to a user than prior alerts.

In other implementations of smart alerts in the context of pumps/pens orother delivery devices, the user interface of such delivery devices maybe employed to acknowledge smart alerts, and conversely the userinterface of a monitoring device may be employed to acknowledge alertsinitiated by the delivery device. Put another way, data about a smartalert generated by one device may be communicated to another deviceusing an appropriate transmission protocol, and rendered and in somecases acknowledged on the other device. If acknowledged, theacknowledgment may be communicated back to the generating device usingan appropriate transmission protocol.

Smart alert functionality as implemented in an open loop system mayprovide smart alerts prompting the user to take various actions, basedon user cognitive awareness. In a closed loop system or a semi closedloop system, considerations may be had of both user cognitive awarenessand machine cognitive awareness, where the latter relates to machineawareness of a diabetic state warranting attention. This implementationis illustrated by the flowchart 450 of FIG. 44. In the flowchart 450,which is also based on the flowchart 101 of FIG. 2, following a step ofdetermining user cognitive awareness (step 17), a step may be employedof determining if the system is cognitively aware of the diabetic statewarranting attention (step 21). If the machine is cognitively aware ofthe state, then again there is no need for a smart alert, and the samecan be suppressed or never generated (step 11). If the machine is notcognitively aware of the state, then a smart alert can be provided (step19). It is noted that the estimation or prediction of user cognitiveawareness is itself a task performed by the machine, but this isdistinct from the machine cognitive awareness of step 19 because thelatter relates to whether a delivery device is connected and is treatingthe diabetic state or preparing to treat the same. For example, machinecognitive awareness may cause a shut off of a pump if the systemdetermines, e.g., based on glucose value and rate of change, that theuser may become hypoglycemic. Such is distinct from a determination ofstep 17 that a user is aware of an impending hypoglycemic potentialityand is taking steps to treat the same, e.g., is part of a typicalpattern that the user treats by consuming carbohydrates.

As an extra step is provided before a smart alert is generated, smartalerts may be generated less frequently in a closed loop system than inan open loop system or in a semi closed loop system, and generally onlywhere the system itself cannot treat the diabetic state warrantingattention.

A particular implementation is described in the context of a userapproaching a hypoglycemic situation. Instead of just using a threshold,where an insulin pump would be automatically shut off upon the crossingof a low glucose threshold, if the result of the estimation orprediction of user cognitive awareness performed by the smart alertsfunctionality is such that the user is estimated or predicted to becognitively aware of the diabetic state warranting attention, then thepump may be caused to shut off sooner than in the case where the user isnot cognitively aware, in order to avoid situations of dangeroushypoglycemia. A typical situation in which the user would be unawarewould be nighttime, when the user is sleeping.

What has been described are systems and methods for providing smartalerts to users, such that users may be alerted to diabetic stateswarranting attention in a more effective way than in prior efforts.

Variations will be understood. For example, systems and methods couldinterface with other applications and employ the same for provision ofalerts. For example, where a user is estimated or predicted by thesystem to be cognitively unaware of an impending hypoglycemic diabeticstate warranting attention, but data from a GPS app on the user smartdevice indicates proximity to a source of food, e.g., a gas station orfood store, an alert may appear on the GPS app about the impendingcondition and the location at which remedial action may be taken.Generally the data from the GPS app will be that which providesinformation about the location of the source of food, but data fromother apps may also be leveraged, e.g., meal tracking apps, which mayalso provide information about restaurant locations. Such an alertgenerally uses an appropriate API between the GPS app and the CGM appand APIs between other employed apps. In some cases, data from the GPSapp will indicate that the user is traveling in a direction towards acommonly accessed food source, e.g., a restaurant the user frequents.Especially of the determination can be made unequivocally, e.g., thereare no other commonly frequented locations in that direction, then suchGPS data may be employed to suppress the generation of a smart alert inthe event of potential mild hypoglycemia, at least temporarily.Generally speaking, GPS data used in this way can be advantageouslyemployed in the estimation or prediction of user cognitive awareness.

Implementations of Systems and Methods According to Present PrinciplesEnabling Smart Alerts/Functionality.

Various systems may be employed to implement smart alert functionalityaccording to present principles. For example, in one implementation, amobile computer device, e.g., smart device, e.g., smart phone, may beemployed that is dedicated to health and diabetes management. The mobilecomputer device may be one that is dedicated to health/diabetesmanagement or may be a more general device that is specially adapted forhealth and diabetes management. The device may be configurable by theuser to suit the user's lifestyle and preferences. The mobile device maybe based on an Android or iOS (or other) operating system and utilize atouchscreen interface. In some cases the operating system may becustomized or controlled for administrative services, e.g., to providedata to the cloud, and to optimize/control elements of the userexperience. The device may include one or more common radiocommunication links, such as Bluetooth® Low Energy. It may also includeWiFi or other data connectivity technology, for remote monitoring, datatransfer, and software updates.

The mobile computer may be used in conjunction with (“tethered” to) awearable computing device such as a Smart Watch. The Watch may alsofunction usefully without direct connection (untethered) to the mobiledevice. Such a watch or other such wearable may include sensors such asheart rate monitors, and data from such monitors may also be employed toinform smart alerts functionality. For example, such monitors may beemployed to determine if a user is exercising (perhaps in combinationwith accelerometer data), and if so, the user may desire more or lessalerts/alarms.

Such wearable devices may also allow the possibility of configuringsmart alerts functionality to render a tactile display, providingsignificant privacy and discretion in social situations, and usefulnessin, e.g., sleep. The smart alerts functionality may be configured to, ifa wearable with a tactile display is detected, first provide the alerton the wearable, followed by alerting on other devices, e.g., smartphones, if the alert or alarm on the wearable is ignored.

The user may select from a curated ecosystem of Apps. This selection mayinclude Apps from, for example, Databetes (meal memory), TrainingPeaks(activity/fitness), Tidepool, MyFitnessPal, Nike+, Withings, etc. Otherapps may be included for retrospective insight/pattern recognition aimedat therapy optimization and for the determination of user cognitiveawareness of their current diabetic state warranting attention. Theecosystem may further include an app that provides basic diabetesmanagement instructions for the newly-diagnosed user (or parent of user)with T1D. A distinct set of instructions may be included for thenewly-diagnosed user with T2D.

The mobile computer allows connectivity of data between user (patient)and clinician, via EMR (e.g. Epic). The mobile computer platform enablesand facilitates user/provider dialogue.

In another particular implementation, the mobile computer may beconfigured such that it does not include functionality unrelated tohealth management.

Safety settings are generally implemented. For example, the smart alertsfunctionality may be disabled until such time as CGM data has beencollected. In this way, the settings may be data-driven and datavalidated. For example, one week of data may be required, two weeks ofdata may be required, one month of data may be required, and so on. Asnoted above, an initial set up or training may be performed withoutdata, or using data from a prior user device (associated with thesubject user). This may be followed up with a subsequent optimization,and in particular the smart alerts functionality may perform a dataanalysis subroutine or diagnostic subroutine to determine that enoughdata has been collected to allow functionality related to smart alerts,e.g., to allow prediction or estimation of user cognitive awareness, ormay alert the user or the HCP to perform the same. Alternatively, thesmart alerts functionality may be automatically implemented, but requireconfirmation from the user or HCP.

As noted previously, users are often annoyed by receiving multiple alertor alarm notifications, and the smart alerts functionality according topresent principles may serve to minimize such annoyances. However, wheremultiple notifications are called for and provided, smart alertsfunctionality may also be configured to allow subsequent notificationsto include additional information, so that the user receives anaggregate of all of the actual or potential alerts. For example, if afirst notification provides an indication that the user may be goinglow, and then the system, via smart alerts functionality, knows to notalert the user again, or to suppress alerts/alarms, until another alertthreshold is reached, then the subsequent notification may include adifferent substantive message, e.g., “you've been low for 20 minutesnow”.

Systems and methods according to present principles may further beemployed to detect missed windows for opportunities or actions. Inparticular, machine learning may be employed to learn when a usertypically takes an action, particularly one that has a positive outcome.Where a similar situation is encountered, but a user does not act in thepositive way, e.g., they are away from their smart device or such andare not aware of the opportunity, a smart alert may subsequentlyprovided regarding the missed opportunity.

What has been described are systems and methods for achieving smartalerts, especially where based on estimated or projected user cognitiveawareness of the diabetic state warranting attention. Numerousvariations of the above will be understood given this description.

Sensor System

FIG. 45 depicts an example system 100, in accordance with some exampleimplementations. The system 100 includes a continuous analyte sensorsystem 8 including sensor electronics 12 and a continuous analyte sensor10. The system 100 may include other devices and/or sensors, such asmedicament delivery pump 2 and glucose meter 4. The continuous analytesensor 10 may be physically connected to sensor electronics 12 and maybe integral with (e.g., non-releasably attached to) or releasablyattachable to the continuous analyte sensor 10. The sensor electronics12, medicament delivery pump 2, and/or glucose meter 4 may couple withone or more devices, such as display devices 14, 16, 18, and/or 20.

In some example implementations, the system 100 may include acloud-based analyte processor 490 configured to analyze analyte data(and/or other patient-related data) provided via network 406 (e.g., viawired, wireless, or a combination thereof) from sensor system 8 andother devices, such as display devices 14-20 and the like, associatedwith the host (also referred to as a user or patient) and generatereports providing high-level information, such as statistics, regardingthe measured analyte over a certain time frame. A full discussion ofusing a cloud-based analyte processing system may be found in U.S.patent application Ser. No. 13/788,375, filed 7 Mar. 2013 and publishedas US-2013/0325352, entitled CALCULATION ENGINE BASED ON HISTOGRAMS,herein incorporated by reference in its entirety. In someimplementations, one or more steps of the factory calibration algorithmcan be performed in the cloud.

In some example implementations, the sensor electronics 12 may includeelectronic circuitry associated with measuring and processing datagenerated by the continuous analyte sensor 10. This generated continuousanalyte sensor data may also include algorithms, which can be used toprocess and calibrate the continuous analyte sensor data, although thesealgorithms may be provided in other ways as well. The sensor electronics12 may include hardware, firmware, software, or a combination thereof,to provide measurement of levels of the analyte via a continuous analytesensor, such as a continuous glucose sensor. An example implementationof the sensor electronics 12 is described further below with respect toFIG. 46.

In one implementation, the factory calibration algorithms describedherein may be performed by the sensor electronics.

The sensor electronics 12 may, as noted, couple (e.g., wirelessly andthe like) with one or more devices, such as display devices 14, 16, 18,and/or 20. The display devices 14, 16, 18, and/or 20 may be configuredfor presenting information (and/or alarming), such as sensor informationtransmitted by the sensor electronics 12 for display at the displaydevices 14, 16, 18, and/or 20.

The display devices may include a relatively small, key fob-like displaydevice 14, a relatively large, hand-held display device 16, a cellularphone 18 (e.g., a smart phone, a tablet, and the like), a computer 20,and/or any other user equipment configured to at least presentinformation (e.g., smart alerts, medicament delivery information,discrete self-monitoring glucose readings, heart rate monitor, caloricintake monitor, and the like). In some cases, the display device may be,e.g., a user's car, if the car is in signal communication with, e.g.,the user's smart phone. For example, the car may be in Bluetoothcommunication or have a Bluetooth pairing with the smart phone. Otherdisplay devices may include televisions, smart refrigerators, and so on.

In one implementation, factory calibration algorithms may be performedat least in part by the display devices.

In some example implementations, the relatively small, key fob-likedisplay device 14 may comprise a wrist watch, a belt, a necklace, apendent, a piece of jewelry, an adhesive patch, a pager, a key fob, aplastic card (e.g., credit card), an identification (ID) card, and/orthe like. This small display device 14 may include a relatively smalldisplay (e.g., smaller than the large display device 16) and may beconfigured to display certain types of displayable sensor informationincluding smart alerts, such as a numerical value, and an arrow, or acolor code.

In some example implementations, the relatively large, hand-held displaydevice 16 may comprise a hand-held receiver device, a palm-top computer,and/or the like. This large display device may include a relativelylarger display (e.g., larger than the small display device 14) and maybe configured to display information including smart alerts, such as agraphical representation of the continuous sensor data including currentand historic sensor data output by sensor system 8.

In some example implementations, the continuous analyte sensor 10comprises a sensor for detecting and/or measuring analytes, and thecontinuous analyte sensor 10 may be configured to continuously detectand/or measure analytes as a non-invasive device, a subcutaneous device,a transdermal device, and/or an intravascular device. In some exampleimplementations, the continuous analyte sensor 10 may analyze aplurality of intermittent blood samples, although other analytes may beused as well.

In some example implementations, the continuous analyte sensor 10 maycomprise a glucose sensor configured to measure glucose in the blood orinterstitial fluid using one or more measurement techniques, such asenzymatic, chemical, physical, electrochemical, spectrophotometric,polarimetric, calorimetric, iontophoretic, radiometric, immunochemical,and the like. In implementations in which the continuous analyte sensor10 includes a glucose sensor, the glucose sensor may comprise any devicecapable of measuring the concentration of glucose and may use a varietyof techniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescence monitoring), toprovide data, such as a data stream, indicative of the concentration ofglucose in a host. The data stream may be sensor data (raw and/orfiltered), which may be converted into a calibrated data stream used toprovide a value of glucose to a host, such as a user, a patient, or acaretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor,a nurse, or any other individual that has an interest in the wellbeingof the host). Moreover, the continuous analyte sensor 10 may beimplanted as at least one of the following types of sensors: animplantable glucose sensor, a transcutaneous glucose sensor, implantedin a host vessel or extracorporeally, a subcutaneous sensor, arefillable subcutaneous sensor, an intravascular sensor.

In some example implementations, smart alerts may be provided to alertthe user when they are running low on diabetic supplies, and then toautomatically reorder or to prompt the user to reorder. For example, insome situations, the system may learn that the user typically orders twodays ahead of time, and the system may be aware of the fact that thesensor has two days' worth of life remaining. In this case, the systemmay provide a smart alert informing the user that it is time to ordernew supplies.

Where user acknowledgments are employed, the same may be acknowledged onreceivers, mobile devices, or other such devices. In some cases, alertsor alarms may be acknowledged on the transmitter directly. In somecases, fingerprint recognition may be employed in the acknowledgments,so that non-users do not deleteriously acknowledge alerts or alarms of auser, thereby potentially causing the user to not receive such an alertor alarm.

Although the disclosure herein refers to some implementations thatinclude a continuous analyte sensor 10 comprising a glucose sensor, thecontinuous analyte sensor 10 may comprise other types of analyte sensorsas well. Moreover, although some implementations refer to the glucosesensor as an implantable glucose sensor, other types of devices capableof detecting a concentration of glucose and providing an output signalrepresentative of glucose concentration may be used as well.Furthermore, although the description herein refers to glucose as theanalyte being measured, processed, and the like, other analytes may beused as well including, for example, ketone bodies (e.g., acetone,acetoacetic acid and beta hydroxybutyric acid, lactate, etc.), glucagon,acetyl-CoA, triglycerides, fatty acids, intermediaries in the citricacid cycle, choline, insulin, cortisol, testosterone, and the like.

FIG. 46 depicts an example of sensor electronics 12, in accordance withsome example implementations. The sensor electronics 12 may includesensor electronics that are configured to process sensor information,such as sensor data, and generate transformed sensor data anddisplayable sensor information, e.g., via a processor module. Forexample, the processor module may transform sensor data into one or moreof the following: filtered sensor data (e.g., one or more filteredanalyte concentration values), raw sensor data, calibrated sensor data(e.g., one or more calibrated analyte concentration values), rate ofchange information, trend information, rate of acceleration/decelerationinformation, sensor diagnostic information, location information,alarm/alert information, calibration information such as may bedetermined by calibration algorithms, smoothing and/or filteringalgorithms of sensor data, and/or the like.

In some embodiments, a processor module 214 is configured to achieve asubstantial portion, if not all, of the data processing, including dataprocessing pertaining to factory calibration. Processor module 214 maybe integral to sensor electronics 12 and/or may be located remotely,such as in one or more of devices 14, 16, 18, and/or 20 and/or cloud490. In some embodiments, processor module 214 may comprise a pluralityof smaller subcomponents or submodules. For example, processor module214 may include an alert module (not shown) or prediction module (notshown), or any other suitable module that may be utilized to efficientlyprocess data. When processor module 214 is made up of a plurality ofsubmodules, the submodules may be located within processor module 214,including within the sensor electronics 12 or other associated devices(e.g., 14, 16, 18, 20 and/or 490). For example, in some embodiments,processor module 214 may be located at least partially within acloud-based analyte processor 490 or elsewhere in network 406.

In some example implementations, the processor module 214 may beconfigured to calibrate the sensor data, and the data storage memory 220may store the calibrated sensor data points as transformed sensor data.Moreover, the processor module 214 may be configured, in some exampleimplementations, to wirelessly receive calibration information from adisplay device, such as devices 14, 16, 18, and/or 20, to enablecalibration of the sensor data from sensor 12. Furthermore, theprocessor module 214 may be configured to perform additional algorithmicprocessing on the sensor data (e.g., calibrated and/or filtered dataand/or other sensor information), and the data storage memory 220 may beconfigured to store the transformed sensor data and/or sensor diagnosticinformation associated with the algorithms. The processor module 214 mayfurther be configured to store and use calibration informationdetermined from a calibration.

In some example implementations, the sensor electronics 12 may comprisean application-specific integrated circuit (ASIC) 205 coupled to a userinterface 222. The ASIC 205 may further include a potentiostat 210, atelemetry module 232 for transmitting data from the sensor electronics12 to one or more devices, such as devices 14, 16, 18, and/or 20, and/orother components for signal processing and data storage (e.g., processormodule 214 and data storage memory 220). Although FIG. 46 depicts ASIC205, other types of circuitry may be used as well, including fieldprogrammable gate arrays (FPGA), one or more microprocessors configuredto provide some (if not all of) the processing performed by the sensorelectronics 12, analog circuitry, digital circuitry, or a combinationthereof.

In the example depicted in FIG. 46, through a first input port 211 forsensor data the potentiostat 210 is coupled to a continuous analytesensor 10, such as a glucose sensor, to generate sensor data from theanalyte. The potentiostat 210 may also provide via data line 212 avoltage to the continuous analyte sensor 10 to bias the sensor formeasurement of a value (e.g., a current and the like) indicative of theanalyte concentration in a host (also referred to as the analog portionof the sensor). The potentiostat 210 may have one or more channelsdepending on the number of working electrodes at the continuous analytesensor 10.

In some example implementations, the potentiostat 210 may include aresistor that translates a current value from the sensor 10 into avoltage value, while in some example implementations, acurrent-to-frequency converter (not shown) may also be configured tointegrate continuously a measured current value from the sensor 10using, for example, a charge-counting device. In some exampleimplementations, an analog-to-digital converter (not shown) may digitizethe analog signal from the sensor 10 into so-called “counts” to allowprocessing by the processor module 214. The resulting counts may bedirectly related to the current measured by the potentiostat 210, whichmay be directly related to an analyte level, such as a glucose level, inthe host.

The telemetry module 232 may be operably connected to processor module214 and may provide the hardware, firmware, and/or software that enablewireless communication between the sensor electronics 12 and one or moreother devices, such as display devices, processors, network accessdevices, and the like. A variety of wireless radio technologies that canbe implemented in the telemetry module 232 include Bluetooth®,Bluetooth® Low-Energy, ANT, ANT+, ZigBee, IEEE 802.11, IEEE 802.16,cellular radio access technologies, radio frequency (RF), inductance(e.g., magnetic), near field communication (NFC), infrared (IR), pagingnetwork communication, magnetic induction, satellite data communication,spread spectrum communication, frequency hopping communication, nearfield communications, and/or the like. In some example implementations,the telemetry module 232 comprises a Bluetooth® chip, althoughBluetooth® technology may also be implemented in a combination of thetelemetry module 232 and the processor module 214.

The processor module 214 may control the processing performed by thesensor electronics 12. For example, the processor module 214 may beconfigured to process data (e.g., counts), from the sensor, filter thedata, calibrate the data, perform fail-safe checking, and/or the like.

In some example implementations, the processor module 214 may comprise adigital filter, such as for example an infinite impulse response (IIR)or a finite impulse response (FIR) filter. This digital filter maysmooth a raw data stream received from sensor 10. Generally, digitalfilters are programmed to filter data sampled at a predetermined timeinterval (also referred to as a sample rate). In some exampleimplementations, such as when the potentiostat 210 is configured tomeasure the analyte (e.g., glucose and/or the like) at discrete timeintervals, these time intervals determine the sampling rate of thedigital filter. In some example implementations, the potentiostat 210may be configured to measure continuously the analyte, for example,using a current-to-frequency converter. In these current-to-frequencyconverter implementations, the processor module 214 may be programmed torequest, at predetermined time intervals (acquisition time), digitalvalues from the integrator of the current-to-frequency converter. Thesedigital values obtained by the processor module 214 from the integratormay be averaged over the acquisition time due to the continuity of thecurrent measurement. As such, the acquisition time may be determined bythe sampling rate of the digital filter.

The processor module 214 may further include a data generator (notshown) configured to generate data packages for transmission to devices,such as the display devices 14, 16, 18, and/or 20. Furthermore, theprocessor module 214 may generate data packets for transmission to theseoutside sources via telemetry module 232. In some exampleimplementations, the data packages may, as noted, be customizable foreach display device, and/or may include any available data, such as atime stamp, displayable sensor information, transformed sensor data, anidentifier code for the sensor and/or sensor electronics 12, raw data,filtered data, calibrated data, rate of change information, trendinformation, error detection or correction, and/or the like.

The processor module 214 may also include a program memory 216 and othermemory 218. The processor module 214 may be coupled to a communicationsinterface, such as a communication port 238, and a source of power, suchas a battery 234. Moreover, the battery 234 may be further coupled to abattery charger and/or regulator 236 to provide power to sensorelectronics 12 and/or charge the battery 234.

The program memory 216 may be implemented as a semi-static memory forstoring data, such as an identifier for a coupled sensor 10 (e.g., asensor identifier (ID)) and for storing code (also referred to asprogram code) to configure the ASIC 205 to perform one or more of theoperations/functions described herein. For example, the program code mayconfigure processor module 214 to process data streams or counts,filter, perform the calibration methods, perform fail-safe checking, andthe like.

The memory 218 may also be used to store information. For example, theprocessor module 214 including memory 218 may be used as the system'scache memory, where temporary storage is provided for recent sensor datareceived from the sensor. In some example implementations, the memorymay comprise memory storage components, such as read-only memory (ROM),random-access memory (RAM), dynamic-RAM, static-RAM, non-static RAM,easily erasable programmable read only memory (EEPROM), rewritable ROMs,flash memory, and the like.

The data storage memory 220 may be coupled to the processor module 214and may be configured to store a variety of sensor information. In someexample implementations, the data storage memory 220 stores one or moredays of continuous analyte sensor data. For example, the data storagememory may store 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20,and/or 30 (or more days) of continuous analyte sensor data received fromsensor 10. The stored sensor information may include one or more of thefollowing: a time stamp, raw sensor data (one or more raw analyteconcentration values), calibrated data, filtered data, transformedsensor data, and/or any other displayable sensor information,calibration information (e.g., reference BG values and/or priorcalibration information such as from factory calibration), sensordiagnostic information, and the like.

The user interface 222 may include a variety of interfaces, such as oneor more buttons 224, a liquid crystal display (LCD) 226, a vibrator 228,an audio transducer (e.g., speaker) 230, a backlight (not shown), and/orthe like. The components that comprise the user interface 222 mayprovide controls to interact with the user (e.g., the host). One or morebuttons 224 may allow, for example, toggle, menu selection, optionselection, status selection, yes/no response to on-screen questions, a“turn off” function (e.g., for an alarm), an “acknowledged” function(e.g., for an alarm), a reset, and/or the like. The LCD 226 may providethe user with, for example, visual data output. The audio transducer 230(e.g., speaker) may provide audible signals in response to triggering ofcertain alerts, such as present and/or predicted hyperglycemic andhypoglycemic conditions. In some example implementations, audiblesignals may be differentiated by tone, volume, duty cycle, pattern,duration, and/or the like. In some example implementations, the audiblesignal may be configured to be silenced (e.g., acknowledged or turnedoff) by pressing one or more buttons 224 on the sensor electronics 12and/or by signaling the sensor electronics 12 using a button orselection on a display device (e.g., key fob, cell phone, and/or thelike). In some cases, an audible alert may be silenced while a visualalert is still rendered. As a particular example, an audible alarm maybe silenced while the user may still be enabled to view a glucose traceand/or a threshold line. In this way, the user is informed that they arestill high or low, while avoiding the annoyance of the audible alert.

Although audio and vibratory alarms are described with respect to FIG.46, other alarming mechanisms may be used as well. For example, in someexample implementations, a tactile alarm is provided including a pokingmechanism configured to “poke” or physically contact the patient inresponse to one or more alarm conditions.

In one exemplary implementation, a default method of alerting may beemployed, e.g., a display on a screen, but if alerts or alarms using thedefault mode are not acknowledged within a predetermined period of time,the alerts and alarms may escalate in subsequent reminder alerts oralarms, and many employ alternate means of notification, including usingconnected devices such as medicament delivery devices. For example, iforiginal or initial alerts and alarms are not acknowledged, on the nextsubsequent or reminder alert or alarm, which may occur, e.g., fiveminutes later, the mobile device and/or connected medicament device mayprovide a supplementary or ancillary alert, e.g., an audible alert,e.g., the cell phone may emit a sound. If this alert or alarm is alsonot acknowledged, and if a pump or pen is connected to the mobile device(for example, in referring to FIG. 45, if the medicament delivery pump 2is connected to a device performing responsive processing, e.g., mobiledevice 18 or processor 490, or even in some cases sensor electronics12), the medicament delivery device (pump or pen) may be caused to emitan audible or tactile or visual alert or alarm, e.g., having a mediumamplitude, e.g., medium volume, for a predetermined period of time,e.g., for 20 seconds. Similarly, if these alerts or alarms are also notacknowledged, and again if the pump or pen is connected to the deviceperforming responsive processing, the same may be caused to emit anaudible or tactile or visual alert or alarm, e.g., having a highamplitude, e.g., high volume, for a predetermined period of time, e.g.,for 20 seconds.

Other means may also be employed for rendering alerts or alarms. Forexample, dogs are increasingly being used as diabetic alert dogs or ascompanion animals. In either case, a component of the system may beconfigured with an ultrasound transmitter such that, if the glucoseconcentration value of a user is approaching a dangerous value, theultrasound transducer is caused to render an ultrasonic pulse. Thediabetic alert dog will generally detect an imminent hypoglycemia orhyperglycemia situation; however, if they did not, the ultrasonic pulsemay provide a reminder. Similarly, a companion animal not trained as adiabetic alert dog may be trained to recognize the pulse as a reason toalert their owner, i.e., the diabetic user. Even if such dogs are notspecifically trained, the ultrasonic pulse may cause a level ofagitation that provides a warning to the user that something is wrong.The ultrasound transmitter may be signally coupled to various parts ofthe system, e.g., the transmitter, the smart phone, a receiver, or otherdevice. As the severity of the danger increases, the ultrasonic tonecould change, so the diabetic companion could be trained to respondaccordingly.

In another example, users may be informed of their glucose alerts,alarms, and notifications, without even looking at their monitoringdevice by use of distinctive haptic or vibratory patterns as may berendered on a smart device or watch. Haptic/vibratory patterns may berendered and structured according to relative urgency or safety concernsusing similar principles of the prioritization of auditory or visualalarms and alerts. For example, higher priority alarms may employ ahigher vibration speed, a lesser space between vibrations, a greaterduration, and vice versa for lower priority alarms. Users may be enabledto conform particular vibratory profiles or curves according to theirown dictates and designs. In this way, users can be informed of alertsor alarms and may even receive information about the type and/or urgencyof alarms, without having to look at a display screen or listen to analarm (which latter may also be annoying as it may disturb others in theuser's vicinity).

The battery 234 may be operatively connected to the processor module 214(and possibly other components of the sensor electronics 12) and providethe necessary power for the sensor electronics 12. In some exampleimplementations, the battery is a Lithium Manganese Dioxide battery,however any appropriately sized and powered battery can be used (e.g.,AAA, Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metalhydride, Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some example implementations, the battery isrechargeable. In some example implementations, a plurality of batteriescan be used to power the system. In yet other implementations, thereceiver can be transcutaneously powered via an inductive coupling, forexample.

A battery charger and/or regulator 236 may be configured to receiveenergy from an internal and/or external charger. In some exampleimplementations, a battery regulator (or balancer) 236 regulates therecharging process by bleeding off excess charge current to allow allcells or batteries in the sensor electronics 12 to be fully chargedwithout overcharging other cells or batteries. In some exampleimplementations, the battery 234 (or batteries) is configured to becharged via an inductive and/or wireless charging pad, although anyother charging and/or power mechanism may be used as well.

One or more communication ports 238, also referred to as externalconnector(s), may be provided to allow communication with other devices,for example a PC communication (com) port can be provided to enablecommunication with systems that are separate from, or integral with, thesensor electronics 12. The communication port, for example, may comprisea serial (e.g., universal serial bus or “USB”) communication port, andallow for communicating with another computer system (e.g., PC, personaldigital assistant or “PDA,” server, or the like). In some exampleimplementations, the sensor electronics 12 is able to transmithistorical data to a PC or other computing device for retrospectiveanalysis by a patient and/or HCP. As another example of datatransmission, factory information may also be sent to the algorithm fromthe sensor or from a cloud data source.

The one or more communication ports 238 may further include a secondinput port 237 in which calibration data may be received, and an outputport 239 which may be employed to transmit calibrated data, or data tobe calibrated, to a receiver or mobile device. FIG. 46 illustrates theseaspects schematically. It will be understood that the ports may beseparated physically, but in alternative implementations a singlecommunication port may provide the functions of both the second inputport and the output port.

Exemplary Embodiments

Non-transitory computer readable medium 1: A non-transitory computerreadable medium, comprising instructions for causing a computingenvironment to perform a method of dynamically adjusting or tuning useralerts based on a cognitive awareness determination, thereby providingdata relevant to treatment of a diabetic state warranting attention, themethod comprising steps of: identifying a current or future diabeticstate warranting attention, the identifying based at least partially ona glucose concentration value; estimating or predicting a cognitiveawareness of the user of the identified current or future diabetic statewarranting attention; and if the result of the estimating or predictingis that the user is cognitively unaware of the identified current orfuture diabetic state warranting attention, then alerting a user with auser prompt on a user interface of a monitoring device, the user promptindicating the diabetic state warranting attention, whereby the user isonly alerted of the diabetic state warranting attention if and at a timethat the user is unaware of the diabetic state warranting attention andthat the notification is effective for the user.

Non-transitory computer readable medium 2: An embodiment ofnon-transitory computer readable medium 1, wherein the alerting isoptimized for cognitive awareness of the patient such that fewer alarmsoccur than would otherwise be provided without consideration of usercognitive awareness.

Non-transitory computer readable medium 3: An embodiment of any ofnon-transitory computer readable media 1-2, wherein the monitoringdevice is a smart phone, a smart watch, a dedicated monitoring device,or a tablet computer.

Non-transitory computer readable medium 4: An embodiment of any ofnon-transitory computer readable media 1-3, whereby over-prompting,repeat prompts, or nuisance prompts are minimized or avoided.

Non-transitory computer readable medium 5: An embodiment of any ofnon-transitory computer readable media 1-4, whereby the user is enabledto build trust in the system, that the system will only alert onnotifications optimized or effective for the user.

Non-transitory computer readable medium 6: An embodiment of any ofnon-transitory computer readable media 1-5, wherein the estimating orpredicting a cognitive awareness of the user includes determining if theidentified current or future diabetic state warranting attentionincludes an atypical glucose trace.

Non-transitory computer readable medium 7: An embodiment ofnon-transitory computer readable medium 6, wherein the atypical glucosetrace includes an atypical pattern or an atypical glucose response.

Non-transitory computer readable medium 8: An embodiment of any ofnon-transitory computer readable media 1-7, wherein the estimating orpredicting a cognitive awareness of the user includes determining if theuser has previously treated a like identified diabetic state warrantingattention by taking an action without a user prompt.

Non-transitory computer readable medium 9: An embodiment ofnon-transitory computer readable medium 8, wherein the action is dosingof a medicament.

Non-transitory computer readable medium 10: An embodiment ofnon-transitory computer readable medium 8, wherein the action is eatinga meal.

Non-transitory computer readable medium 11: An embodiment ofnon-transitory computer readable medium 8, wherein the action isexercising.

Non-transitory computer readable medium 12: An embodiment of any ofnon-transitory computer readable media 1-11, wherein the estimating orpredicting a cognitive awareness of the user includes determining if theuser has entered meal or bolus data, or has requested a boluscalculation.

Non-transitory computer readable medium 13: An embodiment of any ofnon-transitory computer readable media 1-12, wherein the estimating orpredicting a cognitive awareness of the user includes determining ifuser behavior is consistent with cognitive awareness.

Non-transitory computer readable medium 14: An embodiment of any ofnon-transitory computer readable media 1-13, wherein the estimating orpredicting a cognitive awareness of the user includes receiving userinput and basing the estimating or predicting at least in part on thereceived input.

T Non-transitory computer readable medium 15: An embodiment of any ofnon-transitory computer readable media 1-14, wherein the estimating orpredicting a cognitive awareness of the user includes analyzing historicdata of glucose values of the user versus time.

Non-transitory computer readable medium 16: An embodiment of any ofnon-transitory computer readable media 1-15, wherein the steps ofidentifying and estimating or predicting are repeated until such a timeas the user is estimated or predicted to be cognitively unaware of theidentified diabetic state warranting attention, and then performing astep of alerting the user with the user prompt.

Non-transitory computer readable medium 17: An embodiment of any ofnon-transitory computer readable media 1-16, wherein the estimating orpredicting a cognitive awareness of the user includes receiving datafrom an application or website through an appropriate API.

Non-transitory computer readable medium 18: An embodiment of any ofnon-transitory computer readable media 1-17, wherein the estimating orpredicting is based at least partially on location data, namely GPSdata.

Non-transitory computer readable medium 19: An embodiment ofnon-transitory computer readable medium 18, wherein the location data isthat of the user.

Non-transitory computer readable medium 20: An embodiment ofnon-transitory computer readable medium 18, wherein the location data isthat of a follower of the user.

Non-transitory computer readable medium 21: An embodiment of any ofnon-transitory computer readable media 1-20, wherein the estimating orpredicting a cognitive awareness of the user is based at least partiallyon one or more of the following: population data, data associated withbehavioral or contextual information, data associated with a life goalof the user, data associated with a user privacy setting, or acombination of these.

Non-transitory computer readable medium 22: An embodiment of any ofnon-transitory computer readable media 1-21, wherein the estimating orpredicting a cognitive awareness of the user is based at least partiallyon real-time data, and wherein the real-time data includes one or moreof the following: data associated with a GPS application in themonitoring device, data associated with an accelerometer in themonitoring device, data associated with behavioral or contextualinformation, data associated with a location of a follower of the user,data associated with a metabolic rate of the user, data associated witha glycemic urgency index of the user, heart rate data, sweat contentdata, data associated with a wearable sensor of the user, insulin data,or a combination of these.

Non-transitory computer readable medium 23: An embodiment of any ofnon-transitory computer readable media 1-22, wherein the estimating orpredicting a cognitive awareness of the user includes recognizing one ormore individualized patterns associated with the user.

Non-transitory computer readable medium 24: An embodiment ofnon-transitory computer readable medium 23, wherein the individualizedpattern corresponds to an envelope of characteristic analyteconcentration signal traces occurring before or after an event.

Non-transitory computer readable medium 25: An embodiment ofnon-transitory computer readable medium 24, wherein the event isassociated with a meal, exercise, or sleep.

Non-transitory computer readable medium 26: An embodiment ofnon-transitory computer readable medium 25, wherein the determination isthat the user is cognitively unaware if a current signal trace fallsoutside the envelope of characteristic analyte concentration signaltraces.

Non-transitory computer readable medium 27: An embodiment of any ofnon-transitory computer readable media 1-26, wherein the method furthercomprises indicating a confidence level associated with the user prompt.

Non-transitory computer readable medium 28: An embodiment of any ofnon-transitory computer readable media 1-27, wherein if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state warranting attention, then displaying the userprompt immediately.

Non-transitory computer readable medium 29: An embodiment of any ofnon-transitory computer readable media 1-28, wherein the estimating orpredicting is further based on location information of the user, whereinthe location information indicates that the user is within apredetermined threshold proximity of a food store or restaurant.

Non-transitory computer readable medium 30: An embodiment of any ofnon-transitory computer readable media 1-29, wherein if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state that warrants attention, then alerting the userwith the user prompt after a time delay, a duration of the time delaybased on at least the identified diabetic state warranting attention andthe glucose concentration value and/or a glucose concentration valuerate of change.

Non-transitory computer readable medium 31: An embodiment of any ofnon-transitory computer readable media 1-30, wherein the user promptincludes a query for a user to enter data.

Non-transitory computer readable medium 32: An embodiment ofnon-transitory computer readable medium 31, wherein the query requestsdata for the user to enter about dosing, meals or exercise.

Non-transitory computer readable medium 33: An embodiment of any ofnon-transitory computer readable media 1-32, wherein if the user ignoresthe user prompt as determined by data from the user interface or datafrom an accelerometer associated with the monitoring device, and if theuser prompt does not correspond to a danger condition, then storinginformation about the user ignoring the user prompt under priorconditions and using the stored information as part of a subsequentestimating or predicting step.

Non-transitory computer readable medium 34: An embodiment of any ofnon-transitory computer readable media 1-33, wherein the identifying acurrent or future diabetic state warranting attention includesdetermining a clinical value of a glucose concentration and/or a glucoserate of change and/or a glycemic urgency index value.

Non-transitory computer readable medium 35: An embodiment of any ofnon-transitory computer readable media 1-34, wherein identifying acurrent or future diabetic state warranting attention includes measuringa glucose signal signature and comparing the measured signature with aplurality of binned signatures, and classifying the diabetic statewarranting attention into one of a plurality of bins based on thecomparison.

Non-transitory computer readable medium 36: An embodiment of any ofnon-transitory computer readable media 1-35, wherein the identifying acurrent or future diabetic state warranting attention determining one ormore time-based trends in the glucose concentration value, and basingthe identified state on the determined trend.

Non-transitory computer readable medium 37: An embodiment ofnon-transitory computer readable medium 36, wherein the trend correspondto whether the glucose concentration value is hovering within a range oris rising or falling, wherein hovering constitutes staying within apredetermined range for a period of greater than 5 or 10 or 15 or 30minutes.

Non-transitory computer readable medium 38: An embodiment ofnon-transitory computer readable medium 37, wherein fuzzy boundaries areemployed for defining the range.

Non-transitory computer readable medium 39: An embodiment of any ofnon-transitory computer readable media 1-28, further comprisingtransmitting an indication of the diabetic state warranting attention toa medicament pump.

Non-transitory computer readable medium 40: An embodiment ofnon-transitory computer readable medium 39, wherein if the result of theestimating or predicting is that the user is not cognitively aware ofthe diabetic state warranting attention, then further comprisingactivating the medicament pump to provide a medicament bolus.

Non-transitory computer readable medium 41: An embodiment ofnon-transitory computer readable medium 40, wherein the medicament bolusis a meal bolus of insulin.

Non-transitory computer readable medium 42: An embodiment ofnon-transitory computer readable medium 42, if the result of theestimating or predicting is that the user is not cognitively aware ofthe diabetic state warranting attention, then further comprisingactivating the medicament pump to change a basal rate.

Non-transitory computer readable medium 43: An embodiment ofnon-transitory computer readable medium 40, wherein the medicament isinsulin.

Non-transitory computer readable medium 44: An embodiment ofnon-transitory computer readable medium 39, further comprisingdetermining if the medicament pump can treat the diabetic statewarranting attention either fully or partially, and if so, then notalerting the user or altering the user prompt, respectively, as comparedto a case where the medicament pump cannot treat the diabetic state.

Non-transitory computer readable medium 45: An embodiment of any ofnon-transitory computer readable media 1-44, wherein, if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state warranting attention, then determining when toalert the user with the user prompt.

Non-transitory computer readable medium 46: An embodiment of any ofnon-transitory computer readable media 1-45, wherein the user prompt, ifdisplayed, includes a color or arrow instead of or in addition to aglucose concentration value.

Non-transitory computer readable medium 47: An embodiment of any ofnon-transitory computer readable media 1-46, wherein the user prompt, ifdisplayed, includes a prediction of a glucose concentration value.

Non-transitory computer readable medium 48: An embodiment of any ofnon-transitory computer readable media 1-47, wherein the user prompt, ifdisplayed, includes an audible indicator, and wherein the volume of theaudible indicator is automatically adjusted for ambient noise asmeasured by the monitoring device or by a device in signal communicationwith the monitoring device, wherein the adjusting for ambient noiseincludes raising the volume of the audible indicator relative to theambient noise until a threshold level of signal to noise ratio isachieved.

Non-transitory computer readable medium 49: An embodiment of any ofnon-transitory computer readable media 1-48, wherein the user prompt isrelated to a diabetic state warranting attention occurring during aperiod when the user has a glycemic urgency index that is low.

Non-transitory computer readable medium 50: An embodiment of any ofnon-transitory computer readable media 1-49, wherein if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state warranting attention, then alerting the user withthe user prompt after a delay based not on a time duration but on theidentified diabetic state warranting attention and on the glucoseconcentration value and/or a glucose concentration value rate of change.

Non-transitory computer readable medium 51: An embodiment of any ofnon-transitory computer readable media 1-50, wherein if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state warranting attention, then alerting the user withthe user prompt after a delay based not on a time duration but on anindividualized pattern learned by the monitoring device.

Non-transitory computer readable medium 52: An embodiment of any ofnon-transitory computer readable media 1-51, wherein the identifieddiabetic state warranting attention corresponds to an atypical glucoseresponse or an atypical pattern, and wherein the atypical response oratypical pattern is learned by a monitoring device and not by userentry.

Non-transitory computer readable medium 53: An embodiment of any ofnon-transitory computer readable media 1-52, wherein the user prompt isdisplayed with dynamic timing on a predesigned user interface and not onan adaptive user interface.

Non-transitory computer readable medium 54: An embodiment of any ofnon-transitory computer readable media 1-53, wherein if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state warranting attention, then alerting the user withthe user prompt immediately regardless of indications to not alert theuser with a user prompt received from other monitoring deviceapplications.

Non-transitory computer readable medium 55: An embodiment of any ofnon-transitory computer readable media 1-54, wherein if the result ofthe estimating or predicting is that the user is not cognitively awareof the diabetic state warranting attention, then alerting the user withthe user prompt immediately regardless of indications to not alert theuser with a user prompt based on other user-entered data or settings.

Non-transitory computer readable medium 56: An embodiment of any ofnon-transitory computer readable media 1-55, wherein the estimating orpredicting if the user is cognitively aware of the diabetic statewarranting attention is based at least in part on real-time data and notentirely on retrospective data.

Non-transitory computer readable medium 57: An embodiment of any ofnon-transitory computer readable media 1-56, wherein the alerting a userwith a user prompt on a user interface of a monitoring device includesrendering an ultrasonic pulse.

Non-transitory computer readable medium 58: An embodiment of any ofnon-transitory computer readable media 1-57, wherein the alerting a userwith a user prompt on a user interface of a monitoring device includes,if the user ignores a first alerting, then causing a prompt on a seconduser interface of the monitoring device.

Non-transitory computer readable medium 59: An embodiment ofnon-transitory computer readable medium 58, wherein the monitoringdevice is a mobile cellular device, and wherein the second userinterface includes an audio channel.

Non-transitory computer readable medium 60: An embodiment ofnon-transitory computer readable medium 59, wherein the prompt is anaudible prompt and is played through the audio channel.

Non-transitory computer readable medium 61: An embodiment ofnon-transitory computer readable medium 58, wherein the monitoringdevice is a mobile cellular device, and wherein the second userinterface includes a vibratory, haptic, or tactile rendering system.

Non-transitory computer readable medium 62: An embodiment ofnon-transitory computer readable medium 61, wherein the prompt is avibratory prompt and is played through the vibratory, haptic, or tactilerendering system.

Non-transitory computer readable medium 63: An embodiment ofnon-transitory computer readable medium 62, wherein the monitoringdevice is a mobile cellular device, and wherein the second userinterface includes an audio channel, wherein the prompt is an audibleprompt and is played through the audio channel, and wherein thevibratory prompt is caused to be rendered if the audible prompt is notacknowledged by a user.

Non-transitory computer readable medium 64: An embodiment of any ofnon-transitory computer readable media 1-63, wherein the alerting a userwith a user prompt on a user interface of a monitoring device includes,if the user ignores a first alerting, then causing a prompt on a seconduser interface of a second device.

Non-transitory computer readable medium 65: An embodiment ofnon-transitory computer readable medium 64, wherein the second device isa medicament delivery device, and where the prompt is audible orvibratory.

Non-transitory computer readable medium 66: An embodiment of any ofnon-transitory computer readable media 1-65, wherein the alerting a userwith a user prompt on a user interface of a monitoring device includesrendering a haptic or vibratory signal.

Non-transitory computer readable medium 67: An embodiment ofnon-transitory computer readable medium 66, wherein the rendering is onthe monitoring device.

Non-transitory computer readable medium 68: An embodiment ofnon-transitory computer readable medium 66, wherein the rendering is ona device in signal communication with the monitoring device.

Non-transitory computer readable medium 69: An embodiment ofnon-transitory computer readable medium 68, wherein the monitoringdevice is a smart phone and the device in signal communication with themonitoring device is a smart watch.

Non-transitory computer readable medium 70: An embodiment ofnon-transitory computer readable medium 66, wherein the renderingincludes rendering the user prompt in a pattern, the patterncorresponding to the diabetic state warranting attention.

System 71: A system for providing smart alerts corresponding to diabeticstates warranting user attention, comprising: a CGM application runningon a mobile device, the CGM application configured to receive data froma sensor on an at least periodic or occasional basis and to calibrateand display glucose concentration data in clinical units; and a smartalerts application running as a subroutine within the CGM application orrunning as a parallel process with the CGM application on the mobiledevice and receiving data from the CGM application, the smart alertsapplication configured to perform the method contained on the medium ofNon-transitory computer readable medium 1.

Non-transitory computer readable medium 72: A non-transitory computerreadable medium, comprising instructions for causing a computingenvironment to perform a method of safely reducing alerting of users todiabetic states that require attention, the method comprising steps of:identifying a current or future diabetic state warranting attention, theidentifying based at least partially on a glucose concentration value;determining if the identified diabetic state warranting attention isatypical for the user; if a result of the determining is that theidentified diabetic state is atypical for the user, then alerting theuser with a user prompt on a user interface of a monitoring device, theuser prompt indicating the diabetic state warranting attention, wherebythe user is only notified of the diabetic state warranting attention ifthe identified diabetic state is atypical for the user.

Non-transitory computer readable medium 73: An embodiment ofnon-transitory computer readable medium 72, wherein the determining ifthe identified diabetic state warranting attention is atypical for theuser includes determining if the identified diabetic state includes aglucose trace following a pattern that is not typical of other patternsassociated with the user.

Non-transitory computer readable medium 74: An embodiment of any ofnon-transitory computer readable media 72-73, wherein the determining ifthe identified diabetic state warranting attention is atypical for theuser includes determining if the identified diabetic state includes aglucose trace following a trend that is not typical of other trendsassociated with the user.

Non-transitory computer readable medium 75: A non-transitory computerreadable medium, comprising instructions for causing a computingenvironment to perform a method of prompting a user about a diabeticstate that warrants attention, the computing environment in signalcommunication with a medicament delivery device, the user promptoptimized for effectiveness to the user at least in part by beingreduced in number, the user prompt providing data relevant to treatmentof the diabetic state warranting attention, the method comprising stepsof: identifying a current or future diabetic state warranting attention,the identifying based at least partially on a glucose concentrationvalue; performing a first estimating or predicting of a cognitiveawareness of the user of the identified current or future diabetic statewarranting attention; if the result of the first estimating orpredicting is that the user is cognitively unaware of the identifiedcurrent or future diabetic state warranting attention, then performing asecond estimating or predicting of a computer awareness of themedicament delivery device of the identified current or future diabeticstate warranting attention; if the result of the second estimating orpredicting is that the medicament delivery device is unaware of theidentified current or future diabetic state warranting attention, thenalerting the user with a user prompt on a user interface of a monitoringdevice, the user prompt indicating the diabetic state warrantingattention, whereby the user is only notified of the diabetic statewarranting attention if and at a time that both the user and themedicament delivery device are unaware of the diabetic state warrantingattention and that the notification is effective for the user.

Non-transitory computer readable medium 76: An embodiment ofnon-transitory computer readable medium 75, wherein the method furthercomprises steps of determining if the medicament delivery device iscapable of treating the identified current or future diabetic statewarranting attention, and if the result of the determining is that themedicament delivery device is incapable of treating the identifieddiabetic state, then alerting the user with the user prompt.

Non-transitory computer readable medium 77: An embodiment of any ofnon-transitory computer readable media 75-76, wherein the current orfuture diabetic state includes hypoglycemia, and wherein the medicamentdelivery device is an insulin delivery device, and further comprisingshutting off or reducing activity of the insulin delivery device basedon the diabetic state of hypoglycemia.

Non-transitory computer readable medium 78: An embodiment ofnon-transitory computer readable medium 77, wherein the shutting off orreducing activity occurs sooner in the case where the user iscognitively unaware of the hypoglycemia.

Non-transitory computer readable medium 79: An embodiment of any ofnon-transitory computer readable media 75-78, wherein the performing afirst estimating or predicting is based at least partially on userinteraction with the medicament delivery device.

In some continuous analyte sensor systems, an on-skin portion of thesensor electronics may be simplified to minimize complexity and/or sizeof on-skin electronics, for example, providing only raw, calibrated,and/or filtered data to a display device configured to run calibrationand other algorithms required for displaying the sensor data. However,the sensor electronics 12 (e.g., via processor module 214) may beimplemented to execute prospective algorithms used to generatetransformed sensor data and/or displayable sensor information,including, for example, algorithms that: evaluate a clinicalacceptability of reference and/or sensor data, evaluate calibration datafor best calibration based on inclusion criteria, evaluate a quality ofthe calibration, compare estimated analyte values with timecorresponding measured analyte values, analyze a variation of estimatedanalyte values, evaluate a stability of the sensor and/or sensor data,detect signal artifacts (noise), replace signal artifacts, determine arate of change and/or trend of the sensor data, perform dynamic andintelligent analyte value estimation, perform diagnostics on the sensorand/or sensor data, set modes of operation, evaluate the data foraberrancies, estimate or predict cognitive awareness of the user, and/orthe like.

Although separate data storage and program memories are shown in FIG.46, a variety of configurations may be used as well. For example, one ormore memories may be used to provide storage space to support dataprocessing and storage requirements at sensor electronics 12.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In anotherpreferred embodiment, the analyte sensor is a transcutaneous glucosesensor, such as described with reference to U.S. Patent Publication No.US-2006-0020187-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007-0027385-A1, co-pendingU.S. patent application Ser. No. 11/543,396 filed Oct. 4, 2006,co-pending U.S. patent application Ser. No. 11/691,426 filed on Mar. 26,2007, and co-pending U.S. patent application Ser. No. 11/675,063 filedon Feb. 14, 2007. In one alternative embodiment, the continuous glucosesensor comprises a transcutaneous sensor such as described in U.S. Pat.No. 6,565,509 to Say et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises a subcutaneoussensor such as described with reference to U.S. Pat. No. 6,579,690 toBonnecaze et al. or U.S. Pat. No. 6,484,046 to Say et al., for example.In another alternative embodiment, the continuous glucose sensorcomprises a refillable subcutaneous sensor such as described withreference to U.S. Pat. No. 6,512,939 to Colvin et al., for example. Inanother alternative embodiment, the continuous glucose sensor comprisesan intravascular sensor such as described with reference to U.S. Pat.No. 6,477,395 to Schulman et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,424,847 toMastrototaro et al.

The connections between the elements shown in the figures illustrateexemplary communication paths. Additional communication paths, eitherdirect or via an intermediary, may be included to further facilitate theexchange of information between the elements. The communication pathsmay be bi-directional communication paths allowing the elements toexchange information.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure (such as the blocks of FIG.46) may be implemented or performed with a general purpose processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array signal (FPGA) or otherprogrammable logic device (PLD), discrete gate or transistor logic,discrete hardware components or any combination thereof designed toperform the functions described herein. A general purpose processor maybe a microprocessor, but in the alternative, the processor may be anycommercially available processor, controller, microcontroller or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of a DSP and a microprocessor, aplurality of microprocessors, one or more microprocessors in conjunctionwith a DSP core, or any other such configuration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise varioustypes of RAM, ROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium that canbe used to carry or store desired program code in the form ofinstructions or data structures and that can be accessed by a computer.Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio, WiFi, Bluetooth®, RFID, NFC, and microwave are included in thedefinition of medium. Disk and disc, as used herein, includes compactdisc (CD), laser disc, optical disc, digital versatile disc (DVD),floppy disk and Blu-ray® disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers. Thus, insome aspects a computer-readable medium may comprise non-transitorycomputer-readable medium (e.g., tangible media). In addition, in someaspects a computer-readable medium may comprise transitorycomputer-readable medium (e.g., a signal). Combinations of the aboveshould also be included within the scope of computer-readable media.

The methods disclosed herein comprise one or more steps or actions forachieving the described methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Certain aspects may comprise a computer program product for performingthe operations presented herein. For example, such a computer programproduct may comprise a computer-readable medium having instructionsstored (and/or encoded) thereon, the instructions being executable byone or more processors to perform the operations described herein. Forcertain aspects, the computer program product may include packagingmaterial.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

The system and method may be fully implemented in any number ofcomputing devices. Typically, instructions are laid out on computerreadable media, generally non-transitory, and these instructions aresufficient to allow a processor in the computing device to implement themethod of the aspects and embodiments. The computer readable medium maybe a hard drive or solid state storage having instructions that, whenrun, are loaded into random access memory. Inputs to the application,e.g., from the plurality of users or from any one user, may be by anynumber of appropriate computer input devices. For example, users mayemploy a keyboard, mouse, touchscreen, joystick, trackpad, otherpointing device, or any other such computer input device to input datarelevant to the calculations. Data may also be input by way of aninserted memory chip, hard drive, flash drives, flash memory, opticalmedia, magnetic media, or any other type of file-storing medium. Theoutputs may be delivered to a user by way of a video graphics card orintegrated graphics chipset coupled to a display that may be seen by auser. Alternatively, a printer may be employed to output hard copies ofthe results. Given this teaching, any number of other tangible outputswill also be understood to be contemplated. For example, outputs may bestored on a memory chip, hard drive, flash drives, flash memory, opticalmedia, magnetic media, or any other type of output. It should also benoted that the aspects and embodiments may be implemented on any numberof different types of computing devices, e.g., personal computers,laptop computers, notebook computers, net book computers, handheldcomputers, personal digital assistants, mobile phones, smart phones,tablet computers, and also on devices specifically designed for thesepurpose. In one implementation, a user of a smart phone orwi-fi-connected device downloads a copy of the application to theirdevice from a server using a wireless Internet connection. Anappropriate authentication procedure and secure transaction process mayprovide for payment to be made to the seller. The application maydownload over the mobile connection, or over the WiFi or other wirelessnetwork connection. The application may then be run by the user. Such anetworked system may provide a suitable computing environment for animplementation in which a plurality of users provide separate inputs tothe system and method. In the below system where smart alerts arecontemplated, the plural inputs may allow plural users to input relevantdata at the same time.

What is claimed is:
 1. A non-transitory computer readable medium,comprising instructions for causing a computing environment to perform amethod of safely reducing alerting of users to diabetic states thatrequire attention, the method comprising: identifying a current orfuture diabetic state warranting attention for a user, the identifyingbased at least partially on a glucose concentration value of the usersatisfying a threshold glucose concentration value associated with theuser; upon identifying the current or future diabetic state warrantingattention, determining if the identified diabetic state warrantingattention is atypical for the user by comparing the identified diabeticstate warranting attention to previously identified diabetic stateswarranting attention for the user; and in response to determining thatthe identified diabetic state is atypical for the user, alerting theuser with a user prompt on a user interface of a monitoring device, theuser prompt indicating the diabetic state warranting attention, whereinthe user is only notified of the diabetic state warranting attention inresponse to the identified diabetic state being atypical for the user.2. The medium of claim 1, wherein the determining if the identifieddiabetic state warranting attention is atypical for the user includesdetermining if the identified diabetic state includes a glucose tracefollowing a pattern that is not typical of other patterns associatedwith the user.
 3. The medium of claim 1, wherein the determining if theidentified diabetic state warranting attention is atypical for the userincludes determining if the identified diabetic state includes a glucosetrace following a trend that is not typical of other trends associatedwith the user.
 4. A system for providing smart alerts corresponding todiabetic states warranting user attention, the system comprising: acontinuous glucose monitoring (CGM) application configured to run on amobile device, the CGM application further configured to receive datafrom a sensor on an at least periodic or occasional basis and tocalibrate and display glucose concentration data in clinical units; anda smart alerts application configured to run as a subroutine within theCGM application or run as a parallel process with the CGM application onthe mobile device and receive data from the CGM application, the smartalerts application further configured to perform the method contained onthe medium of claim
 1. 5. The medium of claim 1, wherein determining ifthe identified diabetic state warranting attention is atypical for theuser includes determining a similarity between data associated with theidentified diabetic state and data that is typical of the user.